Artificial Intelligence-Based Generation of Sequencing Metadata

ABSTRACT

The technology disclosed uses neural networks to determine analyte metadata by (i) processing input image data derived from a sequence of image sets through a neural network and generating an alternative representation of the input image data, the input image data has an array of units that depicts analytes and their surrounding background, (ii) processing the alternative representation through an output layer and generating an output value for each unit in the array, (iii) thresholding output values of the units and classifying a first subset of the units as background units depicting the surrounding background, and (iv) locating peaks in the output values of the units and classifying a second subset of the units as center units containing centers of the analytes.

PRIORITY APPLICATIONS

This application claims priority to or the benefit of the followingapplications:

-   U.S. Provisional Patent Application No. 62/821,602, entitled    “Training Data Generation for Artificial Intelligence-Based    Sequencing,” filed 21 Mar. 2019 (Attorney Docket No. ILLM    1008-1/IP-1693-PRV);-   U.S. Provisional Patent Application No. 62/821,618, entitled    “Artificial Intelligence-Based Generation of Sequencing Metadata,”    filed 21 Mar. 2019 (Attorney Docket No. ILLM 1008-3/IP-1741-PRV);-   U.S. Provisional Patent Application No. 62/821,681, entitled    “Artificial Intelligence-Based Base Calling,” filed 21 Mar. 2019    (Attorney Docket No. ILLM 1008-4/IP-1744-PRV);-   U.S. Provisional Patent Application No. 62/821,724, entitled    “Artificial Intelligence-Based Quality Scoring,” filed 21 Mar. 2019    (Attorney Docket No. ILLM 1008-7/IP-1747-PRV);-   U.S. Provisional Patent Application No. 62/821,766, entitled    “Artificial Intelligence-Based Sequencing,” filed 21 Mar. 2019    (Attorney Docket No. ILLM 1008-9/IP-1752-PRV);-   NL Application No. 2023310, entitled “Training Data Generation for    Artificial Intelligence-Based Sequencing,” filed 14 Jun. 2019    (Attorney Docket No. ILLM 1008-11/IP-1693-NL);-   NL Application No. 2023311, entitled “Artificial Intelligence-Based    Generation of Sequencing Metadata,” filed 14 Jun. 2019 (Attorney    Docket No. ILLM 1008-12/IP-1741-NL);-   NL Application No. 2023312, entitled “Artificial Intelligence-Based    Base Calling,” filed 14 Jun. 2019 (Attorney Docket No. ILLM    1008-13/IP-1744-NL);-   NL Application No. 2023314, entitled “Artificial Intelligence-Based    Quality Scoring,” filed 14 Jun. 2019 (Attorney Docket No. ILLM    1008-14/IP-1747-NL); and-   NL Application No. 2023316, entitled “Artificial Intelligence-Based    Sequencing,” filed 14 Jun. 2019 (Attorney Docket No. ILLM    1008-15/IP-1752-NL).

US Non-Provisioinal Applications

-   U.S. patent application Ser. No. ______, entitled “Training Data    Generation for Artificial Intelligence-Based Sequencing,” (Attorney    Docket No. ILLM 1008-16/IP-1693-US) filed contemporaneously;-   U.S. patent application Ser. No. ______, entitled “Artificial    Intelligence-Based Base Calling,” (Attorney Docket No. ILLM    1008-18/IP-1744-US) filed contemporaneously;-   U.S. patent application Ser. No. ______, entitled “Artificial    Intelligence-Based Quality Scoring,” (Attorney Docket No. ILLM    1008-19/IP-1747-US) filed contemporaneously;-   U.S. patent application Ser. No. ______, entitled “Artificial    Intelligence-Based Sequencing,” (Attorney Docket No. ILLM    1008-20/IP-1752-US) filed contemporaneously;

PCT Applications

-   PCT Patent Application No. PCT ______, titled “Training Data    Generation for Artificial Intelligence-Based Sequencing,” (Attorney    Docket No. ILLM 1008-21/IP-1693-PCT) filed contemporaneously,    subsequently published as PCT Publication No. WO ______;-   PCT Patent Application No. PCT ______, titled “Artificial    Intelligence-Based Generation of Sequencing Metadata,” (Attorney    Docket No. ILLM 1008-22/IP-1741-PCT) filed contemporaneously,    subsequently published as PCT Publication No. WO ______;-   PCT Patent Application No. PCT ______, titled “Artificial    Intelligence-Based Base Calling,” (Attorney Docket No. ILLM    1008-23/IP-1744-PCT) filed contemporaneously, subsequently published    as PCT Publication No. WO ______;-   PCT Patent Application No. PCT ______, titled “Artificial    Intelligence-Based Quality Scoring,” (Attorney Docket No. ILLM    1008-24/IP-1747-PCT) filed contemporaneously, subsequently published    as PCT Publication No. WO ______;-   PCT Patent Application No. PCT ______, titled “Artificial    Intelligence-Based Sequencing,” (Attorney Docket No. ILLM    1008-25/IP-1752-PCT) filed contemporaneously, subsequently published    as PCT Publication No. WO ______;

The priority applications are hereby incorporated by reference for allpurposes as if fully set forth herein.

INCORPORATIONS

The following are incorporated by reference for all purposes as if fullyset forth herein:

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FIELD OF THE TECHNOLOGY DISCLOSED

The technology disclosed relates to artificial intelligence typecomputers and digital data processing systems and corresponding dataprocessing methods and products for emulation of intelligence (i.e.,knowledge based systems, reasoning systems, and knowledge acquisitionsystems); and including systems for reasoning with uncertainty (e.g.,fuzzy logic systems), adaptive systems, machine learning systems, andartificial neural networks. In particular, the technology disclosedrelates to using deep neural networks such as deep convolutional neuralnetworks for analyzing data.

BACKGROUND

The subject matter discussed in this section should not be assumed to beprior art merely as a result of its mention in this section. Similarly,a problem mentioned in this section or associated with the subjectmatter provided as background should not be assumed to have beenpreviously recognized in the prior art. The subject matter in thissection merely represents different approaches, which in and ofthemselves can also correspond to implementations of the claimedtechnology.

Deep neural networks are a type of artificial neural networks that usemultiple nonlinear and complex transforming layers to successively modelhigh-level features. Deep neural networks provide feedback viabackpropagation which carries the difference between observed andpredicted output to adjust parameters. Deep neural networks have evolvedwith the availability of large training datasets, the power of paralleland distributed computing, and sophisticated training algorithms. Deepneural networks have facilitated major advances in numerous domains suchas computer vision, speech recognition, and natural language processing.

Convolutional neural networks (CNNs) and recurrent neural networks(RNNs) are components of deep neural networks. Convolutional neuralnetworks have succeeded particularly in image recognition with anarchitecture that comprises convolution layers, nonlinear layers, andpooling layers. Recurrent neural networks are designed to utilizesequential information of input data with cyclic connections amongbuilding blocks like perceptrons, long short-term memory units, andgated recurrent units. In addition, many other emergent deep neuralnetworks have been proposed for limited contexts, such as deepspatio-temporal neural networks, multi-dimensional recurrent neuralnetworks, and convolutional auto-encoders.

The goal of training deep neural networks is optimization of the weightparameters in each layer, which gradually combines simpler features intocomplex features so that the most suitable hierarchical representationscan be learned from data. A single cycle of the optimization process isorganized as follows. First, given a training dataset, the forward passsequentially computes the output in each layer and propagates thefunction signals forward through the network. In the final output layer,an objective loss function measures error between the inferenced outputsand the given labels. To minimize the training error, the backward passuses the chain rule to backpropagate error signals and compute gradientswith respect to all weights throughout the neural network. Finally, theweight parameters are updated using optimization algorithms based onstochastic gradient descent. Whereas batch gradient descent performsparameter updates for each complete dataset, stochastic gradient descentprovides stochastic approximations by performing the updates for eachsmall set of data examples. Several optimization algorithms stem fromstochastic gradient descent. For example, the Adagrad and Adam trainingalgorithms perform stochastic gradient descent while adaptivelymodifying learning rates based on update frequency and moments of thegradients for each parameter, respectively.

Another core element in the training of deep neural networks isregularization, which refers to strategies intended to avoid overfittingand thus achieve good generalization performance. For example, weightdecay adds a penalty term to the objective loss function so that weightparameters converge to smaller absolute values. Dropout randomly removeshidden units from neural networks during training and can be consideredan ensemble of possible subnetworks. To enhance the capabilities ofdropout, a new activation function, maxout, and a variant of dropout forrecurrent neural networks called mnDrop have been proposed. Furthermore,batch normalization provides a new regularization method throughnormalization of scalar features for each activation within a mini-batchand learning each mean and variance as parameters.

Given that sequenced data are multi- and high-dimensional, deep neuralnetworks have great promise for bioinformatics research because of theirbroad applicability and enhanced prediction power. Convolutional neuralnetworks have been adapted to solve sequence-based problems in genomicssuch as motif discovery, pathogenic variant identification, and geneexpression inference. Convolutional neural networks use a weight-sharingstrategy that is especially useful for studying DNA because it cancapture sequence motifs, which are short, recurring local patterns inDNA that are presumed to have significant biological functions. Ahallmark of convolutional neural networks is the use of convolutionfilters.

Unlike traditional classification approaches that are based onelaborately-designed and manually-crafted features, convolution filtersperform adaptive learning of features, analogous to a process of mappingraw input data to the informative representation of knowledge. In thissense, the convolution filters serve as a series of motif scanners,since a set of such filters is capable of recognizing relevant patternsin the input and updating themselves during the training procedure.Recurrent neural networks can capture long-range dependencies insequential data of varying lengths, such as protein or DNA sequences.

Therefore, an opportunity arises to use a principled deep learning-basedframework for template generation and base calling.

In the era of high-throughput technology, amassing the highest yield ofinterpretable data at the lowest cost per effort remains a significantchallenge. Cluster-based methods of nucleic acid sequencing, such asthose that utilize bridge amplification for cluster formation, have madea valuable contribution toward the goal of increasing the throughput ofnucleic acid sequencing. These cluster-based methods rely on sequencinga dense population of nucleic acids immobilized on a solid support, andtypically involve the use of image analysis software to deconvolveoptical signals generated in the course of simultaneously sequencingmultiple clusters situated at distinct locations on a solid support.

However, such solid-phase nucleic acid cluster-based sequencingtechnologies still face considerable obstacles that limit the amount ofthroughput that can be achieved. For example, in cluster-basedsequencing methods, determining the nucleic acid sequences of two ormore clusters that are physically too close to one another to beresolved spatially, or that in fact physically overlap on the solidsupport, can pose an obstacle. For example, current image analysissoftware can require valuable time and computational resources fordetermining from which of two overlapping clusters an optical signal hasemanated. As a consequence, compromises are inevitable for a variety ofdetection platforms with respect to the quantity and/or quality ofnucleic acid sequence information that can be obtained.

High density nucleic acid cluster-based genomics methods extend to otherareas of genome analysis as well. For example, nucleic acidcluster-based genomics can be used in sequencing applications,diagnostics and screening, gene expression analysis, epigeneticanalysis, genetic analysis of polymorphisms, and the like. Each of thesenucleic acid cluster-based genomics technologies, too, is limited whenthere is an inability to resolve data generated from closely proximateor spatially overlapping nucleic acid clusters.

Clearly there remains a need for increasing the quality and quantity ofnucleic acid sequencing data that can be obtained rapidly andcost-effectively for a wide variety of uses, including for genomics(e.g., for genome characterization of any and all animal, plant,microbial or other biological species or populations), pharmacogenomics,transcriptomics, diagnostics, prognostics, biomedical risk assessment,clinical and research genetics, personalized medicine, drug efficacy anddrug interactions assessments, veterinary medicine, agriculture,evolutionary and biodiversity studies, aquaculture, forestry,oceanography, ecological and environmental management, and otherpurposes.

The technology disclosed provides neural network-based methods andsystems that address these and similar needs, including increasing thelevel of throughput in high-throughput nucleic acid sequencingtechnologies, and offers other related advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. The color drawings also may be available in PAIRvia the Supplemental Content tab.

In the drawings, like reference characters generally refer to like partsthroughout the different views. Also, the drawings are not necessarilyto scale, with an emphasis instead generally being placed uponillustrating the principles of the technology disclosed. In thefollowing description, various implementations of the technologydisclosed are described with reference to the following drawings, inwhich:

FIG. 1 shows one implementation of a processing pipeline that determinescluster metadata using subpixel base calling.

FIG. 2 depicts one implementation of a flow cell that contains clustersin its tiles.

FIG. 3 illustrates one example of the Illumina GA-IIx flow cell witheight lanes.

FIG. 4 depicts an image set of sequencing images for four-channelchemistry, i.e., the image set has four sequencing images, capturedusing four different wavelength bands (image/imaging channel) in thepixel domain.

FIG. 5 is one implementation of dividing a sequencing image intosubpixels (or subpixel regions).

FIG. 6 shows preliminary center coordinates of the clusters identifiedby the base caller during the subpixel base calling.

FIG. 7 depicts one example of merging subpixel base calls produced overthe plurality of sequencing cycles to generate the so-called “clustermaps” that contain the cluster metadata.

FIG. 8a illustrates one example of a cluster map generated by themerging of the subpixel base calls.

FIG. 8b depicts one implementation of subpixel base calling.

FIG. 9 shows another example of a cluster map that identifies clustermetadata.

FIG. 10 shows how a center of mass (COM) of a disjointed region in acluster map is calculated.

FIG. 11 depicts one implementation of calculation of a weighted decayfactor based on the Euclidean distance from a subpixel in a disjointedregion to the COM of the disjointed region.

FIG. 12 illustrates one implementation of an example ground truth decaymap derived from an example cluster map produced by the subpixel basecalling.

FIG. 13 illustrates one implementation of deriving a ternary map from acluster map.

FIG. 14 illustrates one implementation of deriving a binary map from acluster map.

FIG. 15 is a block diagram that shows one implementation of generatingtraining data that is used to train the neural network-based templategenerator and the neural network-based base caller.

FIG. 16 shows characteristics of the disclosed training examples used totrain the neural network-based template generator and the neuralnetwork-based base caller.

FIG. 17 illustrates one implementation of processing input image datathrough the disclosed neural network-based template generator andgenerating an output value for each unit in an array. In oneimplementation, the array is a decay map. In another implementation, thearray is a ternary map. In yet another implementation, the array is abinary map.

FIG. 18 shows one implementation of post-processing techniques that areapplied to the decay map, the ternary map, or the binary map produced bythe neural network-based template generator to derive cluster metadata,including cluster centers, cluster shapes, cluster sizes, clusterbackground, and/or cluster boundaries.

FIG. 19 depicts one implementation of extracting cluster intensity inthe pixel domain.

FIG. 20 illustrates one implementation of extracting cluster intensityin the subpixel domain.

FIG. 21a shows three different implementations of the neuralnetwork-based template generator.

FIG. 21b depicts one implementation of the input image data that is fedas input to the neural network-based template generator 1512. The inputimage data comprises a series of image sets with sequencing images thatare generated during a certain number of initial sequences cycles of asequencing run.

FIG. 22 shows one implementation of extracting patches from the seriesof image sets in FIG. 21b to produce a series of “down-sized” image setsthat form the input image data.

FIG. 23 depicts one implementation of upsampling the series of imagesets in FIG. 21b to produce a series of “upsampled” image sets thatforms the input image data.

FIG. 24 shows one implementation of extracting patches from the seriesof upsampled image sets in FIG. 23 to produce a series of “upsampled anddown-sized” image sets that form the input image data.

FIG. 25 illustrates one implementation of an overall example process ofgenerating ground truth data for training the neural network-basedtemplate generator.

FIG. 26 illustrates one implementation of the regression model.

FIG. 27 depicts one implementation of generating a ground truth decaymap from a cluster map. The ground truth decay map is used as groundtruth data for training the regression model.

FIG. 28 is one implementation of training the regression model using abackpropagation-based gradient update technique.

FIG. 29 is one implementation of template generation by the regressionmodel during inference.

FIG. 30 illustrates one implementation of subjecting the decay map topost-processing to identify cluster metadata.

FIG. 31 depicts one implementation of a watershed segmentation techniqueidentifying non-overlapping groups of contiguous cluster/clusterinterior subpixels that characterize the clusters.

FIG. 32 is a table that shows an example U-Net architecture of theregression model.

FIG. 33 illustrates different approaches of extracting cluster intensityusing cluster shape information identified in a template image.

FIG. 34 shows different approaches of base calling using the outputs ofthe regression model.

FIG. 35 illustrates the difference in base calling performance when theRTA base caller uses ground truth center of mass (COM) location as thecluster center, as opposed to using a non-COM location as the clustercenter. The results show that using COM improves base calling.

FIG. 36 shows, on the left, an example decay map produced the regressionmodel. On the right, FIG. 36 also shows an example ground truth decaymap that the regression model approximates during the training.

FIG. 37 portrays one implementation of the peak locator identifyingcluster centers in the decay map by detecting peaks.

FIG. 38 compares peaks detected by the peak locator in a decay mapproduced by the regression model with peaks in a corresponding groundtruth decay map.

FIG. 39 illustrates performance of the regression model using precisionand recall statistics.

FIG. 40 compares performance of the regression model with the RTA basecaller for 20 pM library concentration (normal run).

FIG. 41 compares performance of the regression model with the RTA basecaller for 30 pM library concentration (dense run).

FIG. 42 compares number of non-duplicate proper read pairs, i.e., thenumber of paired reads that do not have both reads aligned inwardswithin a reasonable distance detected by the regression model versus thesame detected by the RTA base caller.

FIG. 43 shows, on the right, a first decay map produced by theregression model. On the left, FIG. 43 shows a second decay map producedby the regression model.

FIG. 44 compares performance of the regression model with the RTA basecaller for 40 pM library concentration (highly dense run).

FIG. 45 shows, on the left, a first decay map produced by the regressionmodel. On the right, FIG. 45 shows the results of the thresholding, thepeak locating, and the watershed segmentation technique applied to thefirst decay map.

FIG. 46 illustrates one implementation of the binary classificationmodel.

FIG. 47 is one implementation of training the binary classificationmodel using a backpropagation-based gradient update technique thatinvolves softmax scores.

FIG. 48 is another implementation of training the binary classificationmodel using a backpropagation-based gradient update technique thatinvolves sigmoid scores.

FIG. 49 illustrates another implementation of the input image data fedto the binary classification model and the corresponding class labelsused to train the binary classification model.

FIG. 50 is one implementation of template generation by the binaryclassification model during inference.

FIG. 51 illustrates one implementation of subjecting the binary map topeak detection to identify cluster centers.

FIG. 52a shows, on the left, an example binary map produced by thebinary classification model. On the right, FIG. 52a also shows anexample ground truth binary map that the binary classification modelapproximates during the training.

FIG. 52b illustrates performance of the binary classification modelusing a precision statistic.

FIG. 53 is a table that shows an example architecture of the binaryclassification model.

FIG. 54 illustrates one implementation of the ternary classificationmodel.

FIG. 55 is one implementation of training the ternary classificationmodel using a backpropagation-based gradient update technique.

FIG. 56 illustrates another implementation of the input image data fedto the ternary classification model and the corresponding class labelsused to train the ternary classification model.

FIG. 57 is a table that shows an example architecture of the ternaryclassification model.

FIG. 58 is one implementation of template generation by the ternaryclassification model during inference.

FIG. 59 shows a ternary map produced by the ternary classificationmodel.

FIG. 60 depicts an array of units produced by the ternary classificationmodel 5400, along with the unit-wise output values.

FIG. 61 shows one implementation of subjecting the ternary map topost-processing to identify cluster centers, cluster background, andcluster interior.

FIG. 62a shows example predictions of the ternary classification model.

FIG. 62b illustrates other example predictions of the ternaryclassification model.

FIG. 62c shows yet other example predictions of the ternaryclassification model.

FIG. 63 depicts one implementation of deriving the cluster centers andcluster shapes from the output of the ternary classification model inFIG. 62 a.

FIG. 64 compares base calling performance of the binary classificationmodel, the regression model, and the RTA base caller.

FIG. 65 compares the performance of the ternary classification modelwith that of the RTA base caller under three contexts, five sequencingmetrics, and two run densities.

FIG. 66 compares the performance of the regression model with that ofthe RTA base caller under the three contexts, the five sequencingmetrics, and the two run densities discussed in FIG. 65.

FIG. 67 focuses on the penultimate layer of the neural network-basedtemplate generator.

FIG. 68 visualizes what the penultimate layer of the neuralnetwork-based template generator has learned as a result of thebackpropagation-based gradient update training. The illustratedimplementation visualizes twenty-four out of the thirty-two trainedconvolution filters of the penultimate layer depicted in FIG. 67.

FIG. 69 overlays cluster center predictions of the binary classificationmodel (in blue) onto those of the RTA base caller (in pink).

FIG. 70 overlays cluster center predictions made by the RTA base caller(in pink) onto visualization of the trained convolution filters of thepenultimate layer of the binary classification model.

FIG. 71 illustrates one implementation of training data used to trainthe neural network-based template generator.

FIG. 72 is one implementation of using beads for image registrationbased on cluster center predictions of the neural network-based templategenerator.

FIG. 73 illustrates one implementation of cluster statistics of clustersidentified by the neural network-based template generator.

FIG. 74 shows how the neural network-based template generator's abilityto distinguish between adjacent clusters improves when the number ofinitial sequencing cycles for which the input image data is usedincreases from five to seven.

FIG. 75 illustrates the difference in base calling performance when aRTA base caller uses ground truth center of mass (COM) location as thecluster center, as opposed to when a non-COM location is used as thecluster center.

FIG. 76 portrays the performance of the neural network-based templategenerator on extra detected clusters.

FIG. 77 shows different datasets used for training the neuralnetwork-based template generator.

FIGS. 78A and 78B depict one implementation of a sequencing system. Thesequencing system comprises a configurable processor.

FIG. 79 is a simplified block diagram of a system for analysis of sensordata from the sequencing system, such as base call sensor outputs.

FIG. 80 is a simplified diagram showing aspects of the base callingoperation, including functions of a runtime program executed by a hostprocessor.

FIG. 81 is a simplified diagram of a configuration of a configurableprocessor such as the one depicted in FIG. 79.

FIG. 82 is a computer system that can be used by the sequencing systemof FIG. 78A to implement the technology disclosed herein.

DETAILED DESCRIPTION

The following discussion is presented to enable any person skilled inthe art to make and use the technology disclosed, and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed implementations will be readily apparentto those skilled in the art, and the general principles defined hereinmay be applied to other implementations and applications withoutdeparting from the spirit and scope of the technology disclosed. Thus,the technology disclosed is not intended to be limited to theimplementations shown, but is to be accorded the widest scope consistentwith the principles and features disclosed herein.

INTRODUCTION

Base calling from digital images is massively parallel andcomputationally intensive. This presents numerous technical challengesthat we identify before introducing our new technology.

The signal from an image set being evaluated is increasingly faint asclassification of bases proceeds in cycles, especially over increasinglylong strands of bases. The signal-to-noise ratio decreases as baseclassification extends over the length of a strand, so reliabilitydecreases. Updated estimates of reliability are expected as theestimated reliability of base classification changes.

Digital images are captured from amplified clusters of sample strands.Samples are amplified by duplicating strands using a variety of physicalstructures and chemistries. During sequencing by synthesis, tags arechemically attached in cycles and stimulated to glow. Digital sensorscollect photons from the tags that are read out of pixels to produceimages.

Interpreting digital images to classify bases requires resolvingpositional uncertainty, handicapped by limited image resolution. At agreater resolution than collected during base calling, it is apparentimaged clusters have irregular shapes and indeterminate centerpositions. Cluster positions are not mechanically regulated, so clustercenters are not aligned with pixel centers. A pixel center can be theinteger coordinate assigned to a pixel. In other implementations, it canbe the top-left corner of the pixel. In yet other implementations, itcan be the centroid or center-of-mass of the pixel. Amplification doesnot produce uniform cluster shapes. Distribution of cluster signals inthe digital image is, therefore, a statistical distribution rather thana regular pattern. We call this positional uncertainty.

One of the signal classes may produce no detectable signal and beclassified at a particular position based on a “dark” signal. Thus,templates are necessary for classification during dark cycles.Production of templates resolves initial positional uncertainty usingmultiple imaging cycles to avoid missing dark signals.

Trade-offs in image sensor size, magnification, and stepper design leadto pixel sizes that are relatively large, that are too large to treatcluster centers as coincident with sensor pixel centers. This disclosureuses pixel in two senses. The physical, sensor pixel is a region of anoptical sensor that reports detected photons. A logical pixel, simplyreferred to as a pixel, is data corresponding to at least one physicalpixel, data read from the sensor pixel. The pixel can be subdivided or“up sampled” into sub pixels, such as 4×4 sub pixels. To take intoaccount the possibility that all the photons are hitting one side of thephysical pixel and not the opposite side, values can be assigned to subpixels by interpolation, such as bilinear interpolation or areaweighting. Interpolation or bilinear interpolation also is applied whenpixels are re-framed by applying an affine transformation to data fromphysical pixels.

Larger physical pixels are more sensitive to faint signals than smallerpixels. While digital sensors improve with time, the physical limitationof collector surface area is unavoidable Taking design trade-offs intoconsideration, legacy systems have been designed to collect and analyzeimage data from a three-by-three patch of sensor pixels, with the centerof the cluster somewhere in the center pixel of the patch.

High resolution sensors capture only part of an imaged media at a time.The sensor is stepped over the imaged media to cover the whole field.Thousands of digital images can be collected during one processingcycle.

Sensor and illumination design are combined to distinguish among atleast four illumination response values that are used to classify bases.If a traditional RGB camera with a Bayer color filter array were used,four sensor pixels would be combined into a single RGB value. This wouldreduce the effective sensor resolution by four-fold. Alternatively,multiple images can be collected at a single position using differentillumination wavelengths and/or different filters rotated into positionbetween the imaged media and the sensor. The number of images requiredto distinguish among four base classifications varies between systems.Some systems use one image with four intensity levels for differentclasses of bases. Other systems use two images with differentillumination wavelengths (red and green, for instance) and/or filterswith a sort of truth table to classify bases. Systems also can use fourimages with different illumination wavelengths and/or filters tuned tospecific base classes.

Massively parallel processing of digital images is practically necessaryto align and combine relatively short strands, on the order of 30 to2000 base pairs, into longer sequences, potentially millions or evenbillions of bases in length. Redundant samples are desirable over animaged media, so a part of a sequence may be covered by dozens of samplereads. Millions or at least hundreds of thousands of sample clusters areimaged from a single imaged media. Massively parallel processing of somany clusters has increased in sequencing capacity while decreasingcost.

The capacity for sequencing has increased at a pace that rivals Moore'slaw. While the first sequencing cost billions of dollars, in 2018services such as Illumina™ are delivering results for hundred(s) ofdollars. As sequencing goes mainstream and unit prices drop, lesscomputing power is available for classification, which increases thechallenge of near real time classification. With these technicalchallenges in mind, we turn to the technology disclosed.

The technology disclosed improves processing during both templategeneration to resolve positional uncertainty and during baseclassification of clusters at resolved positions. Applying thetechnology disclosed, less expensive hardware can be used to reduce thecost of machines. Near real time analysis can become cost effective,reducing the lag between image collection and base classification.

The technology disclosed can use upsampled images produced byinterpolating sensor pixels into subpixels and then producing templatesthat resolve positional uncertainty. A resulting subpixel is submittedto a base caller for classification that treats the subpixel as if itwere at the center of a cluster. Clusters are determined from groups ofadjoining subpixels that repeatedly receive the same baseclassification. This aspect of the technology leverages existing basecalling technology to determine shapes of clusters and to hyper-locatecluster centers with a subpixel resolution.

Another aspect of the technology disclosed is to create ground truth,training data sets that pair images with confidently determined clustercenters and/or cluster shapes. Deep learning systems and other machinelearning approaches require substantial training sets. Human curateddata is expensive to compile. The technology disclosed can be used toleverage existing classifiers, in a non-standard mode of operation, togenerate large sets of confidently classified training data withoutintervention or the expense of a human curator. The training datacorrelates raw images with cluster centers and/or cluster shapesavailable from existing classifiers, in a non-standard mode ofoperation, such as CNN-based deep learning systems, which can thendirectly process image sequences. One training image can be rotated andreflected to produce additional, equally valid examples. Trainingexamples can focus on regions of a predetermined size within an overallimage. The context evaluated during base calling determines the size ofexample training regions, rather than the size of an image from oroverall imaged media.

The technology disclosed can produce different types of maps, usable astraining data or as templates for base classification, which correlatecluster centers and/or cluster shapes with digital images. First, asubpixel can be classified as a cluster center, thereby localizing acluster center within a physical sensor pixel. Second, a cluster centercan be calculated as the centroid of a cluster shape. This location canbe reported with a selected numeric precision. Third, a cluster centercan be reported with surrounding subpixels in a decay map, either atsubpixel or pixel resolution. A decay map reduces weight given tophotons detected in regions as separation of the regions from thecluster center increase, attenuating signals from more distantpositions. Fourth, binary or ternary classifications can be applied tosubpixels or pixels in clusters of adjoining regions. In binaryclassification, a region is classified as belonging to a cluster centeror as background. In ternary classification, the third class type isassigned to the region that contains the cluster interior, but not thecluster center. Subpixel classification of cluster center locationscould be substituted for real valued cluster center coordinates within alarger optical pixel.

The alternative styles of maps can initially be produced as ground truthdata sets, or, with training, they can be produced using a neuralnetwork. For instance, clusters can be depicted as disjoint regions ofadjoining subpixels with appropriate classifications. Intensity mappedclusters from a neural network can be post-processed by a peak detectorfilter, to calculate cluster centers, if the centers have not alreadybeen determined. Applying a so-called watershed analysis, abuttingregions can be assigned to separate clusters. When produced by a neuralnetwork inference engine, the maps can be used as templates forevaluating a sequence of digital images and classifying bases overcycles of base calling.

Neural Network-Based Template Generation

The first step of template generation is determining cluster metadata.Cluster metadata identifies spatial distribution of clusters, includingtheir centers, shapes, sizes, background, and/or boundaries.

Determining Cluster Metadata

FIG. 1 shows one implementation of a processing pipeline that determinescluster metadata using subpixel base calling.

FIG. 2 depicts one implementation of a flow cell that contains clustersin its tiles. The flow cell is partitioned into lanes. The lanes arefurther partitioned into non-overlapping regions called “tiles”. Duringthe sequencing procedure, the clusters and their surrounding backgroundon the tiles are imaged.

FIG. 3 illustrates an example Illumina GA-IIx™ flow cell with eightlanes. FIG. 3 also shows a zoom-in on one tile and its clusters andtheir surrounding background.

FIG. 4 depicts an image set of sequencing images for four-channelchemistry, i.e., the image set has four sequencing images, capturedusing four different wavelength bands (image/imaging channel) in thepixel domain. Each image in the image set covers a tile of a flow celland depicts intensity emissions of clusters on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. In one implementation, each imaged channelcorresponds to one of a plurality of filter wavelength bands. In anotherimplementation, each imaged channel corresponds to one of a plurality ofimaging events at a sequencing cycle. In yet another implementation,each imaged channel corresponds to a combination of illumination with aspecific laser and imaging through a specific optical filter. Theintensity emissions of a cluster comprise signals detected from ananalyte that can be used to classify a base associated with the analyte.For example, the intensity emissions may be signals indicative ofphotons emitted by tags that are chemically attached to an analyteduring a cycle when the tags are stimulated and that may be detected byone or more digital sensors, as described above.

FIG. 5 is one implementation of dividing a sequencing image intosubpixels (or subpixel regions). In the illustrated implementation,quarter (0.25) subpixels are used, which results in each pixel in thesequencing image being divided into sixteen subpixels. Given that theillustrated sequencing image has a resolution of 20×20 pixels, i.e., 400pixels, the division produces 6400 subpixels. Each of the subpixels istreated by a base caller as a region center for subpixel base calling.In some implementations, this base caller does not use neuralnetwork-based processing. In other implementations, this base caller isa neural network-based base caller.

For a given sequencing cycle and a particular subpixel, the base calleris configured with logic to produce a base call for the given sequencingcycle particular subpixel by performing image processing steps andextracting intensity data for the subpixel from the corresponding imageset of the sequencing cycle. This is done for each of the subpixels andfor each of a plurality of sequencing cycles. Experiments have also beencarried out with quarter subpixel division of 1800×1800 pixel resolutiontile images of the Illumina MiSeq sequencer. Subpixel base calling wasperformed for fifty sequencing cycles and for ten tiles of a lane.

FIG. 6 shows preliminary center coordinates of the clusters identifiedby the base caller during the subpixel base calling. FIG. 6 also shows“origin subpixels” or “center subpixels” that contain the preliminarycenter coordinates.

FIG. 7 depicts one example of merging subpixel base calls produced overthe plurality of sequencing cycles to generate the so-called “clustermaps” that contain the cluster metadata. In the illustratedimplementation, the subpixel base calls are merged using a breadth-firstsearch approach.

FIG. 8a illustrates one example of a cluster map generated by themerging of the subpixel base calls. FIG. 8b depicts one example ofsubpixel base calling. FIG. 8b also shows one implementation ofanalyzing subpixel-wise base call sequences produced from the subpixelbase calling to generate a cluster map.

Sequencing Images

Cluster metadata determination involves analyzing image data produced bya sequencing instrument 102 (e.g., Illumina's iSeq, HiSeqX, HiSeq3000,HiSeq4000, HiSeq2500, NovaSeq 6000, NextSeq, NextSeqDx, MiSeq andMiSeqDx). The following discussion outlines how the image data isgenerated and what it depicts, in accordance with one implementation.

Base calling is the process in which the raw signal of the sequencinginstrument 102, i.e., intensity data extracted from images, is decodedinto DNA sequences and quality scores. In one implementation, theIllumina platforms employ cyclic reversible termination (CRT) chemistryfor base calling. The process relies on growing nascent DNA strandscomplementary to template DNA strands with modified nucleotides, whiletracking an emitted signal of each newly added nucleotide. The modifiednucleotides have a 3′ removable block that anchors a fluorophore signalof the nucleotide type.

Sequencing occurs in repetitive cycles, each comprising three steps: (a)extension of a nascent strand by adding a modified nucleotide; (b)excitation of the fluorophores using one or more lasers of the opticalsystem 104 and imaging through different filters of the optical system104, yielding sequencing images 108; and (c) cleavage of thefluorophores and removal of the 3′ block in preparation for the nextsequencing cycle. Incorporation and imaging cycles are repeated up to adesignated number of sequencing cycles, defining the read length of allclusters. Using this approach, each cycle interrogates a new positionalong the template strands.

The tremendous power of the Illumina platforms stems from their abilityto simultaneously execute and sense millions or even billions ofclusters undergoing CRT reactions. The sequencing process occurs in aflow cell 202—a small glass slide that holds the input DNA fragmentsduring the sequencing process. The flow cell 202 is connected to thehigh-throughput optical system 104, which comprises microscopic imaging,excitation lasers, and fluorescence filters. The flow cell 202 comprisesmultiple chambers called lanes 204. The lanes 204 are physicallyseparated from each other and may contain different tagged sequencinglibraries, distinguishable without sample cross contamination. Theimaging device 106 (e.g., a solid-state imager such as a charge-coupleddevice (CCD) or a complementary metal-oxide-semiconductor (CMOS) sensor)takes snapshots at multiple locations along the lanes 204 in a series ofnon-overlapping regions called tiles 206.

For example, there are a hundred tiles per lane in Illumina GenomeAnalyzer II and sixty-eight tiles per lane in Illumina HiSeq2000. A tile206 holds hundreds of thousands to millions of clusters. An imagegenerated from a tile with clusters shown as bright spots is shown at208. A cluster 302 comprises approximately one thousand identical copiesof a template molecule, though clusters vary in size and shape. Theclusters are grown from the template molecule, prior to the sequencingrun, by bridge amplification of the input library. The purpose of theamplification and cluster growth is to increase the intensity of theemitted signal since the imaging device 106 cannot reliably sense asingle fluorophore. However, the physical distance of the DNA fragmentswithin a cluster 302 is small, so the imaging device 106 perceives thecluster of fragments as a single spot 302.

The output of a sequencing run is the sequencing images 108, eachdepicting intensity emissions of clusters on the tile in the pixeldomain for a specific combination of lane, tile, sequencing cycle, andfluorophore (208A, 208C, 208T, 208G).

In one implementation, a biosensor comprises an array of light sensors.A light sensor is configured to sense information from a correspondingpixel area (e.g., a reaction site/well/nanowell) on the detectionsurface of the biosensor. An analyte disposed in a pixel area is said tobe associated with the pixel area, i.e., the associated analyte. At asequencing cycle, the light sensor corresponding to the pixel area isconfigured to detect/capture/sense emissions/photons from the associatedanalyte and, in response, generate a pixel signal for each imagedchannel. In one implementation, each imaged channel corresponds to oneof a plurality of filter wavelength bands. In another implementation,each imaged channel corresponds to one of a plurality of imaging eventsat a sequencing cycle. In yet another implementation, each imagedchannel corresponds to a combination of illumination with a specificlaser and imaging through a specific optical filter.

Pixel signals from the light sensors are communicated to a signalprocessor coupled to the biosensor (e.g., via a communication port). Foreach sequencing cycle and each imaged channel, the signal processorproduces an image whose pixels respectivelydepict/contain/denote/represent/characterize pixel signals obtained fromthe corresponding light sensors. This way, a pixel in the imagecorresponds to: (i) a light sensor of the biosensor that generated thepixel signal depicted by the pixel, (ii) an associated analyte whoseemissions were detected by the corresponding light sensor and convertedinto the pixel signal, and (iii) a pixel area on the detection surfaceof the biosensor that holds the associated analyte.

Consider, for example, that a sequencing run uses two different imagedchannels: a red channel and a green channel. Then, at each sequencingcycle, the signal processor produces a red image and a green image. Thisway, for a series of k sequencing cycles of the sequencing run, asequence with k pairs of red and green images is produced as output.

Pixels in the red and green images (i.e., different imaged channels)have one-to-one correspondence within a sequencing cycle. This meansthat corresponding pixels in a pair of the red and green images depictintensity data for the same associated analyte, albeit in differentimaged channels. Similarly, pixels across the pairs of red and greenimages have one-to-one correspondence between the sequencing cycles.This means that corresponding pixels in different pairs of the red andgreen images depict intensity data for the same associated analyte,albeit for different acquisition events/timesteps (sequencing cycles) ofthe sequencing run.

Corresponding pixels in the red and green images (i.e., different imagedchannels) can be considered a pixel of a “per-cycle image” thatexpresses intensity data in a first red channel and a second greenchannel. A per-cycle image whose pixels depict pixel signals for asubset of the pixel areas, i.e., a region (tile) of the detectionsurface of the biosensor, is called a “per-cycle tile image.” A patchextracted from a per-cycle tile image is called a “per-cycle imagepatch” In one implementation, the patch extraction is performed by aninput preparer.

The image data comprises a sequence of per-cycle image patches generatedfor a series of k sequencing cycles of a sequencing run. The pixels inthe per-cycle image patches contain intensity data for associatedanalytes and the intensity data is obtained for one or more imagedchannels (e.g., a red channel and a green channel) by correspondinglight sensors configured to detect emissions from the associatedanalytes. In one implementation, when a single target cluster is to bebase called, the per-cycle image patches are centered at a center pixelthat contains intensity data for a target associated analyte andnon-center pixels in the per-cycle image patches contain intensity datafor associated analytes adjacent to the target associated analyte. Inone implementation, the image data is prepared by an input preparer.

Subpixel Base Calling

The technology disclosed accesses a series of image sets generatedduring a sequencing run. The image sets comprise the sequencing images108. Each image set in the series is captured during a respectivesequencing cycle of the sequencing run. Each image (or sequencing image)in the series captures clusters on a tile of a flow cell and theirsurrounding background.

In one implementation, the sequencing run utilizes four-channelchemistry and each image set has four images. In another implementation,the sequencing run utilizes two-channel chemistry and each image set hastwo images. In yet another implementation, the sequencing run utilizesone-channel chemistry and each image set has two images. In yet otherimplementations, each image set has only one image.

The sequencing images 108 in the pixel domain are first converted intothe subpixel domain by a subpixel addresser 110 to produce sequencingimages 112 in the subpixel domain. In one implementation, each pixel inthe sequencing images 108 is divided into sixteen subpixels 502. Thus,in one implementation, the subpixels 502 are quarter subpixels. Inanother implementation, the subpixels 502 are half subpixels. As aresult, each of the sequencing images 112 in the subpixel domain has aplurality of subpixels 502.

The subpixels are then separately fed as input to a base caller 114 toobtain, from the base caller 114, a base call classifying each of thesubpixels as one of four bases (A, C, T, and G). This produces a basecall sequence 116 for each of the subpixels across a plurality ofsequencing cycles of the sequencing run. In one implementation, thesubpixels 502 are identified to the base caller 114 based on theirinteger or non-integer coordinates. By tracking the emission signal fromthe subpixels 502 across image sets generated during the plurality ofsequencing cycles, the base caller 114 recovers the underlying DNAsequence for each subpixel. An example of this is illustrated in FIG. 8b.

In other implementations, the technology disclosed obtains, from thebase caller 114, the base call classifying each of the subpixels as oneof five bases (A, C, T, G, and N). In such implementations, N base calldenotes an undecided base call, usually due to low levels of extractedintensity.

Some examples of the base caller 114 include non-neural network-basedIllumina offerings such as the RTA (Real Time Analysis), the Firecrestprogram of the Genome Analyzer Analysis Pipeline, the IPAR (IntegratedPrimary Analysis and Reporting) machine, and the OLB (Off-LineBasecaller). For example, the base caller 114 produces the base callsequences by interpolating intensity of the subpixels, including atleast one of nearest neighbor intensity extraction, Gaussian basedintensity extraction, intensity extraction based on average of 2×2subpixel area, intensity extraction based on brightest of 2×2 subpixelarea, intensity extraction based on average of 3×3 subpixel area,bilinear intensity extraction, bicubic intensity extraction, and/orintensity extraction based on weighted area coverage. These techniquesare described in detail in Appendix entitled “Intensity ExtractionMethods”.

In other implementations, the base caller 114 can be a neuralnetwork-based base caller, such as the neural network-based base caller1514 disclosed herein.

The subpixel-wise base call sequences 116 are then fed as input to asearcher 118. The searcher 118 searches for substantially matching basecall sequences of contiguous subpixels. Base call sequences ofcontiguous subpixels are “substantially matching” when a predeterminedportion of base calls match on an ordinal position-wise basis(e.g., >=41 matches in 45 cycles, <=4 mismatches in 45 cycles, <=4mismatches in 50 cycles, or <=2 mismatches in 34 cycles).

The searcher 118 then generates a cluster map 802 that identifiesclusters as disjointed regions, e.g., 804 a-d, of contiguous subpixelsthat share a substantially matching base call sequence. This applicationuses “disjointed”, “disjoint”, and “non-overlapping” interchangeably.The search involves base calling the subpixels that contain parts ofclusters to allow linking the called subpixels to contiguous subpixelswith which they share a substantially matching base call sequence. Insome implementations, the searcher 118 requires that at least some ofthe disjointed regions have a predetermined minimum number of subpixels(e.g., more than 4, 6, or 10 subpixels) to be processed as a cluster.

In some implementations, the base caller 114 also identifies preliminarycenter coordinates of the clusters. Subpixels that contain thepreliminary center coordinates are referred to as origin subpixels. Someexample preliminary center coordinates (604 a-c) identified by the basecaller 114 and corresponding origin subpixels (606 a-c) are shown inFIG. 6. However, identification of the origin subpixels (preliminarycenter coordinates of the clusters) is not needed, as explained below.In some implementations, the searcher 118 uses breadth-first search foridentifying substantially matching base call sequences of the subpixelsby beginning with the origin subpixels 606 a-c and continuing withsuccessively contiguous non-origin subpixels 702 a-c. This again isoptional, as explained below.

Cluster Map

FIG. 8a illustrates one example of a cluster map 802 generated by themerging of the subpixel base calls. The cluster map identifies aplurality of disjointed regions (depicted in various colors in FIG. 8a). Each disjointed region comprises a non-overlapping group ofcontiguous subpixels that represents a respective cluster on a tile(from whose sequencing images and for which the cluster map is generatedvia the subpixel base calling). The region between the disjointedregions represents the background on the tile. The subpixels in thebackground region are called “background subpixels”. The subpixels inthe disjointed regions are called “cluster subpixels” or “clusterinterior subpixels”. In this discussion, origin subpixels are thosesubpixels in which preliminary center cluster coordinates determined bythe RTA or another base caller, are located.

The origin subpixels contain the preliminary center cluster coordinates.This means that the area covered by an origin subpixel includes acoordinate location that coincides with a preliminary center clustercoordinate location. Since the cluster map 802 is an image of logicalsubpixels, the origin subpixels are some of the subpixels in the clustermap.

The search to identify clusters with substantially matching base callsequences of the subpixels does not need to begin with identification ofthe origin subpixels (preliminary center coordinates of the clusters)because the search can be done for all the subpixels and can start fromany subpixel (e.g., 0,0 subpixel or any random subpixel). Thus, sinceeach subpixel is evaluated to determine whether it shares asubstantially matching base call sequence with another contiguoussubpixel, the search does not depend on origin subpixels; the search canstart with any subpixel.

Irrespective of whether origin subpixels are used or not, certainclusters are identified that do not contain the origin subpixels(preliminary center coordinates of the clusters) predicted by the basecaller 114. Some examples of clusters identified by the merging of thesubpixel base calls and not containing an origin subpixel are clusters812 a, 812 b, 812 c, 812 d, and 812 e in FIG. 8a . Thus, the technologydisclosed identifies additional or extra clusters for which the centersmay not have been identified by the base caller 114. Therefore, use ofthe base caller 114 for identification of origin subpixels (preliminarycenter coordinates of the clusters) is optional and not essential forthe search of substantially matching base call sequences of contiguoussubpixels.

In one implementation, first, the origin subpixels (preliminary centercoordinates of the clusters) identified by the base caller 114 are usedto identify a first set of clusters (by identification of substantiallymatching base call sequences of contiguous subpixels). Then, subpixelsthat are not part of the first set of clusters are used to identify asecond set of clusters (by identification of substantially matching basecall sequences of contiguous subpixels). This allows the technologydisclosed to identify additional or extra clusters for which the centersare not identified by the base caller 114. Finally, subpixels that arenot part of the first and second sets of clusters are identified asbackground subpixels.

FIG. 8b depicts one example of subpixel base calling. In FIG. 8b , eachsequencing cycle has an image set with four distinct images (i.e., A, C,T, G images) captured using four different wavelength bands(image/imaging channel) and four different fluorescent dyes (one foreach base).

In this example, pixels in images are divided into sixteen subpixels.Subpixels are then separately base called at each sequencing cycle bythe base caller 114. To base call a given subpixel at a particularsequencing cycle, the base caller 114 uses intensities of the givensubpixel in each of the four A, C, T, G images. For example, intensitiesin image regions covered by subpixel 1 in each of the four A, C, T, Gimages of cycle 1 are used to base call subpixel 1 at cycle 1. Forsubpixel 1, these image regions include top-left one-sixteenth area ofthe respective top-left pixels in each of the four A, C, T, G images ofcycle 1. Similarly, intensities in image regions covered by subpixel min each of the four A, C, T, G images of cycle n are used to base callsubpixel mat cycle n. For subpixel m, these image regions includebottom-right one-sixteenth area of the respective bottom-right pixels ineach of the four A, C, T, G images of cycle 1.

This process produces subpixel-wise base call sequences 116 across theplurality of sequencing cycles. Then, the searcher 118 evaluates pairsof contiguous subpixels to determine whether they have a substantiallymatching base call sequence. If yes, then the pair of subpixels isstored in the cluster map 802 as belonging to a same cluster in adisjointed region. If no, then the pair of subpixels is stored in thecluster map 802 as not belonging to a same disjointed region. Thecluster map 802 therefore identifies contiguous sets of sub-pixels forwhich the base calls for the sub-pixels substantially match across aplurality of cycles. Cluster map 802 therefore uses information frommultiple cycles to provide a plurality of clusters with a highconfidence that each cluster of the plurality of clusters providessequence data for a single DNA strand.

A cluster metadata generator 122 then processes the cluster map 802 todetermine cluster metadata, including determining spatial distributionof clusters, including their centers (810 a), shapes, sizes, background,and/or boundaries based on the disjointed regions (FIG. 9).

In some implementations, the cluster metadata generator 122 identifiesas background those subpixels in the cluster map 802 that do not belongto any of the disjointed regions and therefore do not contribute to anyclusters. Such subpixels are referred to as background subpixels 806a-c.

In some implementations, the cluster map 802 identifies cluster boundaryportions 808 a-c between two contiguous subpixels whose base callsequences do not substantially match.

The cluster map is stored in memory (e.g., cluster maps data store 120)for use as ground truth for training a classifier such as the neuralnetwork-based template generator 1512 and the neural network-based basecaller 1514. The cluster metadata can also be stored in memory (e.g.,cluster metadata data store 124).

FIG. 9 shows another example of a cluster map that identifies clustermetadata, including spatial distribution of the clusters, along withcluster centers, cluster shapes, cluster sizes, cluster background,and/or cluster boundaries.

Center of Mass (COM)

FIG. 10 shows how a center of mass (COM) of a disjointed region in acluster map is calculated. The COM can be used as the “revised” or“improved” center of the corresponding cluster in downstream processing.

In some implementations, a center of mass generator 1004, on acluster-by-cluster basis, determines hyperlocated center coordinates1006 of the clusters by calculating centers of mass of the disjointedregions of the cluster map as an average of coordinates of respectivecontiguous subpixels forming the disjointed regions. It then stores thehyperlocated center coordinates of the clusters in the memory on thecluster-by-cluster basis for use as ground truth for training theclassifier.

In some implementations, a subpixel categorizer, on thecluster-by-cluster basis, identifies centers of mass subpixels 1008 inthe disjointed regions 804 a-d of the cluster map 802 at thehyperlocated center coordinates 1006 of the clusters.

In other implementations, the cluster map is upsampled usinginterpolation. The upsampled cluster map is stored in the memory for useas ground truth for training the classifier.

Decay Factor & Decay Map

FIG. 11 depicts one implementation of calculation of a weighted decayfactor for a subpixel based on the Euclidean distance from the subpixelto the center of mass (COM) of the disjointed region to which thesubpixel belongs. In the illustrated implementation, the weighted decayfactor gives the highest value to the subpixel containing the COM anddecreases for subpixels further away from the COM. The weighted decayfactor is used to derive a ground truth decay map 1204 from a clustermap generated from the subpixel base calling discussed above. The groundtruth decay map 1204 contains an array of units and assigns at least oneoutput value to each unit in the array. In some implementations, theunits are subpixels and each subpixel is assigned an output value basedon the weighted decay factor. The ground truth decay map 1204 is thenused as ground truth for training the disclosed neural network-basedtemplate generator 1512. In some implementations, information from theground truth decay map 1204 is also used to prepare input for thedisclosed neural network-based base caller 1514.

FIG. 12 illustrates one implementation of an example ground truth decaymap 1204 derived from an example cluster map produced by the subpixelbase calling as discussed above. In some implementations, in theupsampled cluster map, on the cluster-by-cluster basis, a value isassigned to each contiguous subpixel in the disjointed regions based ona decay factor 1102 that is proportional to distance 1106 of acontiguous subpixel from a center of mass subpixel 1104 in a disjointedregion to which the contiguous subpixel belongs.

FIG. 12 depicts a ground truth decay map 1204. In one implementation,the subpixel value is an intensity value normalized between zero andone. In another implementation, in the upsampled cluster map, a samepredetermined value is assigned to all the subpixels identified as thebackground. In some implementations, the predetermined value is a zerointensity value.

In some implementations, the ground truth decay map 1204 is generated bya ground truth decay map generator 1202 from the upsampled cluster mapthat expresses the contiguous subpixels in the disjointed regions andthe subpixels identified as the background based on their assignedvalues. The ground truth decay map 1204 is stored in the memory for useas ground truth for training the classifier. In one implementation, eachsubpixel in the ground truth decay map 1204 has a value normalizedbetween zero and one.

Ternary (Three Class) Map

FIG. 13 illustrates one implementation of deriving a ground truthternary map 1304 from a cluster map. The ground truth ternary map 1304contains an array of units and assigns at least one output value to eachunit in the array. By name, ternary map implementations of the groundtruth ternary map 1304 assign three output values to each unit in thearray, such that, for each unit, a first output value corresponds to aclassification label or score for a background class, a second outputvalue corresponds to a classification label or score for a clustercenter class, and a third output value corresponds to a classificationlabel or score for a cluster/cluster interior class. The ground truthternary map 1304 is used as ground truth data for training the neuralnetwork-based template generator 1512. In some implementations,information from the ground truth ternary map 1304 is also used toprepare input for the neural network-based base caller 1514.

FIG. 13 depicts an example ground truth ternary map 1304. In anotherimplementation, in the upsampled cluster map, the contiguous subpixelsin the disjointed regions are categorized on the cluster-by-clusterbasis by a ground truth ternary map generator 1302, as cluster interiorsubpixels belonging to a same cluster, the centers of mass subpixels ascluster center subpixels, and as background subpixels the subpixels notbelonging to any cluster. In some implementations, the categorizationsare stored in the ground truth ternary map 1304. These categorizationsand the ground truth ternary map 1304 are stored in the memory for useas ground truth for training the classifier.

In other implementations, on the cluster-by-cluster basis, coordinatesof the cluster interior subpixels, the cluster center subpixels, and thebackground subpixels are stored in the memory for use as ground truthfor training the classifier. Then, the coordinates are downscaled by afactor used to upsample the cluster map. Then, on the cluster-by-clusterbasis, the downscaled coordinates are stored in the memory for use asground truth for training the classifier.

In yet other implementations, the ground truth ternary map generator1302 uses the cluster maps to generate the ternary ground truth data1304 from the upsampled cluster map. The ternary ground truth data 1304labels the background subpixels as belonging to a background class, thecluster center subpixels as belonging to a cluster center class, and thecluster interior subpixels as belonging to a cluster interior class. Insome visualization implementations, color coding can be used to depictand distinguish the different class labels. The ternary ground truthdata 1304 is stored in the memory for use as ground truth for trainingthe classifier.

Binary (Two Class) Map

FIG. 14 illustrates one implementation of deriving a ground truth binarymap 1404 from a cluster map. The binary map 1404 contains an array ofunits and assigns at least one output value to each unit in the array.By name, the binary map assigns two output values to each unit in thearray, such that, for each unit, a first output value corresponds to aclassification label or score for a cluster center class and a secondoutput value corresponds to a classification label or score for anon-center class. The binary map is used as ground truth data fortraining the neural network-based template generator 1512. In someimplementations, information from the binary map is also used to prepareinput for the neural network-based base caller 1514.

FIG. 14 depicts a ground truth binary map 1404. The ground truth binarymap generator 1402 uses the cluster maps 120 to generate the binaryground truth data 1404 from the upsampled cluster maps. The binaryground truth data 1404 labels the cluster center subpixels as belongingto a cluster center class and labels all other subpixels as belonging toa non-center class. The binary ground truth data 1404 is stored in thememory for use as ground truth for training the classifier.

In some implementations, the technology disclosed generates cluster maps120 for a plurality of tiles of the flow cell, stores the cluster mapsin memory, and determines spatial distribution of clusters in the tilesbased on the cluster maps 120, including their shapes and sizes. Then,the technology disclosed, in the upsampled cluster maps 120 of theclusters in the tiles, categorizes, on a cluster-by-cluster basis,subpixels as cluster interior subpixels belonging to a same cluster,cluster center subpixels, and background subpixels. The technologydisclosed then stores the categorizations in the memory for use asground truth for training the classifier, and stores, on thecluster-by-cluster basis across the tiles, coordinates of the clusterinterior subpixels, the cluster center subpixels, and the backgroundsubpixels in the memory for use as ground truth for training theclassifier. The technology disclosed then downscales the coordinates bythe factor used to upsample the cluster map and stores, on thecluster-by-cluster basis across the tiles, the downscaled coordinates inthe memory for use as ground truth for training the classifier.

In some implementations, the flow cell has at least one patternedsurface with an array of wells that occupy the clusters. In suchimplementations, based on the determined shapes and sizes of theclusters, the technology disclosed determines: (1) which ones of thewells are substantially occupied by at least one cluster, (2) which onesof the wells are minimally occupied, and (3) which ones of the wells areco-occupied by multiple clusters. This allows for determining respectivemetadata of multiple clusters that co-occupy a same well, i.e., centers,shapes, and sizes of two or more clusters that share a same well.

In some implementations, the solid support on which samples areamplified into clusters comprises a patterned surface. A “patternedsurface” refers to an arrangement of different regions in or on anexposed layer of a solid support. For example, one or more of theregions can be features where one or more amplification primers arepresent. The features can be separated by interstitial regions whereamplification primers are not present. In some implementations, thepattern can be an x-y format of features that are in rows and columns.In some implementations, the pattern can be a repeating arrangement offeatures and/or interstitial regions. In some implementations, thepattern can be a random arrangement of features and/or interstitialregions. Exemplary patterned surfaces that can be used in the methodsand compositions set forth herein are described in U.S. Pat. Nos.8,778,849, 9,079,148, 8,778,848, and US Pub. No. 2014/0243224, each ofwhich is incorporated herein by reference.

In some implementations, the solid support comprises an array of wellsor depressions in a surface. This may be fabricated as is generallyknown in the art using a variety of techniques, including, but notlimited to, photolithography, stamping techniques, molding techniquesand microetching techniques. As will be appreciated by those in the art,the technique used will depend on the composition and shape of the arraysubstrate.

The features in a patterned surface can be wells in an array of wells(e.g. microwells or nanowells) on glass, silicon, plastic or othersuitable solid supports with patterned, covalently-linked gel such aspoly(N-(5-azidoacetamidylpentyl)acrylamide-co-acrylamide) (PAZAM, see,for example, US Pub. No. 2013/184796, WO 2016/066586, and WO2015-002813, each of which is incorporated herein by reference in itsentirety). The process creates gel pads used for sequencing that can bestable over sequencing runs with a large number of cycles. The covalentlinking of the polymer to the wells is helpful for maintaining the gelin the structured features throughout the lifetime of the structuredsubstrate during a variety of uses. However in many implementations, thegel need not be covalently linked to the wells. For example, in someconditions silane free acrylamide (SFA, see, for example, U.S. Pat. No.8,563,477, which is incorporated herein by reference in its entirety)which is not covalently attached to any part of the structuredsubstrate, can be used as the gel material.

In particular implementations, a structured substrate can be made bypatterning a solid support material with wells (e.g. microwells ornanowells), coating the patterned support with a gel material (e.g.PAZAM, SFA or chemically modified variants thereof, such as theazidolyzed version of SFA (azido-SFA)) and polishing the gel coatedsupport, for example via chemical or mechanical polishing, therebyretaining gel in the wells but removing or inactivating substantiallyall of the gel from the interstitial regions on the surface of thestructured substrate between the wells. Primer nucleic acids can beattached to gel material. A solution of target nucleic acids (e.g. afragmented human genome) can then be contacted with the polishedsubstrate such that individual target nucleic acids will seed individualwells via interactions with primers attached to the gel material;however, the target nucleic acids will not occupy the interstitialregions due to absence or inactivity of the gel material. Amplificationof the target nucleic acids will be confined to the wells since absenceor inactivity of gel in the interstitial regions prevents outwardmigration of the growing nucleic acid colony. The process isconveniently manufacturable, being scalable and utilizing micro- ornano-fabrication methods.

The term “flow cell” as used herein refers to a chamber comprising asolid surface across which one or more fluid reagents can be flowed.Examples of flow cells and related fluidic systems and detectionplatforms that can be readily used in the methods of the presentdisclosure are described, for example, in Bentley et al., Nature456:53-59 (2008), WO 04/018497; U.S. Pat. No. 7,057,026; WO 91/06678; WO07/123744; U.S. Pat. Nos. 7,329,492; 7,211,414; 7,315,019; 7,405,281,and US 2008/0108082, each of which is incorporated herein by reference.

Throughout this disclosure, the terms “P5” and “P7” are used whenreferring to amplification primers. It will be understood that anysuitable amplification primers can be used in the methods presentedherein, and that the use of P5 and P7 are exemplary implementationsonly. Uses of amplification primers such as P5 and P7 on flow cells isknown in the art, as exemplified by the disclosures of WO 2007/010251,WO 2006/064199, WO 2005/065814, WO 2015/106941, WO 1998/044151, and WO2000/018957, each of which is incorporated by reference in its entirety.For example, any suitable forward amplification primer, whetherimmobilized or in solution, can be useful in the methods presentedherein for hybridization to a complementary sequence and amplificationof a sequence. Similarly, any suitable reverse amplification primer,whether immobilized or in solution, can be useful in the methodspresented herein for hybridization to a complementary sequence andamplification of a sequence. One of skill in the art will understand howto design and use primer sequences that are suitable for capture, andamplification of nucleic acids as presented herein.

In some implementations, the flow cell has at least one nonpatternedsurface and the clusters are unevenly scattered over the nonpatternedsurface.

In some implementations, density of the clusters ranges from about100,000 clusters/mm² to about 1,000,000 clusters/mm². In otherimplementations, density of the clusters ranges from about 1,000,000clusters/mm² to about 10,000,000 clusters/mm².

In one implementation, the preliminary center coordinates of theclusters determined by the base caller are defined in a template imageof the tile. In some implementations, a pixel resolution, an imagecoordinate system, and measurement scales of the image coordinate systemare same for the template image and the images.

In another implementation, the technology disclosed relates todetermining metadata about clusters on a tile of a flow cell. First, thetechnology disclosed accesses (1) a set of images of the tile capturedduring a sequencing run and (2) preliminary center coordinates of theclusters determined by a base caller.

Then, for each image set, the technology disclosed obtains a base callclassifying, as one of four bases, (1) origin subpixels that contain thepreliminary center coordinates and (2) a predetermined neighborhood ofcontiguous subpixels that are successively contiguous to respective onesof the origin subpixels. This produces a base call sequence for each ofthe origin subpixels and for each of the predetermined neighborhood ofcontiguous subpixels. The predetermined neighborhood of contiguoussubpixels can be a m×n subpixel patch centered at subpixels containingthe origin subpixels. In one implementation, the subpixel patch is 3×3subpixels. In other implementations, the image patch can be of any size,such as 5×5, 15×15, 20×20, and so on. In other implementations, thepredetermined neighborhood of contiguous subpixels can be a re-connectedsubpixel neighborhood centered at subpixels containing the originsubpixels.

In one implementation, the technology disclosed identifies as backgroundthose subpixels in the cluster map that do not belong to any of thedisjointed regions.

Then, the technology disclosed generates a cluster map that identifiesthe clusters as disjointed regions of contiguous subpixels that: (a) aresuccessively contiguous to at least some of the respective ones of theorigin subpixels and (b) share a substantially matching base callsequence of the one of four bases with the at least some of therespective ones of the origin subpixels.

The technology disclosed then stores the cluster map in memory anddetermines the shapes and the sizes of the clusters based on thedisjointed regions in the cluster map. In other implementations, centersof the clusters are also determined.

Generating Training Data for Template Generator

FIG. 15 is a block diagram that shows one implementation of generatingtraining data that is used to train the neural network-based templategenerator 1512 and the neural network-based base caller 1514.

FIG. 16 shows characteristics of the disclosed training examples used totrain the neural network-based template generator 1512 and the neuralnetwork-based base caller 1514. Each training example corresponds to atile and is labelled with a corresponding ground truth datarepresentation. In some implementations, the ground truth datarepresentation is a ground truth mask or a ground truth map thatidentifies the ground truth cluster metadata in the form of the groundtruth decay map 1204, the ground truth ternary map 1304, or the groundtruth binary map 1404. In some implementation, multiple trainingexamples correspond to a same tile.

In one implementation, the technology disclosed relates to generatingtraining data 1504 for neural network-based template generation and basecalling. First, the technology disclosed accesses a multitude of images108 of a flow cell 202 captured over a plurality of cycles of asequencing run. The flow cell 202 has a plurality of tiles. In themultitude of images 108, each of the tiles has a sequence of image setsgenerated over the plurality of cycles. Each image in the sequence ofimage sets 108 depicts intensity emissions of clusters 302 and theirsurrounding background 304 on a particular one of the tiles at aparticular one the cycles.

Then, a training set constructor 1502 constructs a training set 1504that has a plurality of training examples. As shown in FIG. 16, eachtraining example corresponds to a particular one of the tiles andincludes image data from at least some image sets in the sequence ofimage sets 1602 of the particular one of the tiles. In oneimplementation, the image data includes images in at least some imagesets in the sequence of image sets 1602 of the particular one of thetiles. For example, the images can have a resolution of 1800×1800. Inother implementations, it can be any resolution such as 100×100,3000×3000, 10000×10000, and so on. In yet other implementations, theimage data includes at least one image patch from each of the images. Inone implementation, the image patch covers a portion of the particularone of the tiles. In one example, the image patch can have a resolutionof 20×20. In other implementations, the image patch can have anyresolution, such as 50×50, 70×70, 90×90, 100×100, 3000×3000,10000×10000, and so on.

In some implementations, the image data includes an upsampledrepresentation of the image patch. The upsampled representation can havea resolution of 80×80, for example. In other implementations, theupsampled representation can have any resolution, such as 50×50, 70×70,90×90, 100×100, 3000×3000, 10000×10000, and so on.

In some implementations, multiple training examples correspond to a sameparticular one of the tiles and respectively include as image datadifferent image patches from each image in each of at least some imagesets in a sequence of image sets 1602 of the same particular one of thetiles. In such implementations, at least some of the different imagepatches overlap with each other.

Then, a ground truth generator 1506 generates at least one ground truthdata representation for each of the training examples. The ground truthdata representation identifies at least one of spatial distribution ofclusters and their surrounding background on the particular one of thetiles whose intensity emissions are depicted by the image data,including at least one of cluster shapes, cluster sizes, and/or clusterboundaries, and/or centers of the clusters.

In one implementation, the ground truth data representation identifiesthe clusters as disjointed regions of contiguous subpixels, the centersof the clusters as centers of mass subpixels within respective ones ofthe disjointed regions, and their surrounding background as subpixelsthat do not belong to any of the disjointed regions.

In one implementation, the ground truth data representation has anupsampled resolution of 80×80. In other implementations, the groundtruth data representation can have any resolution, such as 50×50, 70×70,90×90, 100×100, 3000×3000, 10000×10000, and so on.

In one implementation, the ground truth data representation identifieseach subpixel as either being a cluster center or a non-center. Inanother implementation, the ground truth data representation identifieseach subpixel as either being cluster interior, cluster center, orsurrounding background.

In some implementations, the technology disclosed stores, in memory, thetraining examples in the training set 1504 and associated ground truthdata 1508 as the training data 1504 for training the neuralnetwork-based template generator 1512 and the neural network-based basecaller 1514. The training is operationalized by trainer 1510.

In some implementations, the technology disclosed generates the trainingdata for a variety of flow cells, sequencing instruments, sequencingprotocols, sequencing chemistries, sequencing reagents, and clusterdensities.

Neural Network-Based Template Generator

In an inference or production implementation, the technology discloseduses peak detection and segmentation to determine cluster metadata. Thetechnology disclosed processes input image data 1702 derived from aseries of image sets 1602 through a neural network 1706 to generate analternative representation 1708 of the input image data 1702. Forexample, an image set can be for a particular sequencing cycle andinclude four images, one for each image channel A, C, T, and G. Then,for a sequencing run with fifty sequencing cycles, there will be fiftysuch image sets, i.e., a total of 200 images. When arranged temporally,fifty image sets with four images-per image set would form the series ofimage sets 1602. In some implementations, image patches of a certainsize are extracted from each image in the fifty image sets, formingfifty image patch sets with four image patches-per image patch set and,in one implementation, this is the input image data 1702. In otherimplementations, the input image data 1702 comprises image patch setswith four image patches-per image patch set for fewer than the fiftysequencing cycles, i.e., just one, two, three, fifteen, twentysequencing cycles.

FIG. 17 illustrates one implementation of processing input image data1702 through the neural network-based template generator 1512 andgenerating an output value for each unit in an array. In oneimplementation, the array is a decay map 1716. In anotherimplementation, the array is a ternary map 1718. In yet anotherimplementation, the array is a binary map 1720. The array may thereforerepresent one or more properties of each of a plurality of locationsrepresented in the input image data 1702.

Different than training the template generator using structures inearlier figures, including the ground truth decay map 1204, the groundtruth ternary map 1304, and the ground truth binary 1404, the decay map1716, the ternary map 1718, and/or the binary map 1720 are generated byforward propagation of the trained neural network-based templategenerator 1512. The forward propagation can be during training or duringinference. During the training, due to the backward propagation-basedgradient update, the decay map 1716, the ternary map 1718, and thebinary map 1720 (i.e., cumulatively the output 1714) progressively matchor approach the ground truth decay map 1204, the ground truth ternarymap 1304, and the ground truth binary map 1404, respectively.

The size of the image array analyzed during inference depends on thesize of the input image data 1702 (e.g., be the same or an upscaled ordownscaled version), according to one implementation. Each unit canrepresent a pixel, a subpixel, or a superpixel. The unit-wise outputvalues of an array can characterize/represent/denote the decay map 1716,the ternary map 1718, or the binary map 1720. In some implementations,the input image data 1702 is also an array of units in the pixel,subpixel, or superpixel resolution. In such an implementation, theneural network-based template generator 1512 uses semantic segmentationtechniques to produce an output value for each unit in the input array.Additional details about the input image data 1702 can be found in FIGS.21b , 22, 23, and 24 and their discussion.

In some implementations, the neural network-based template generator1512 is a fully convolutional network, such as the one described in J.Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks forsemantic segmentation,” in CVPR, (2015), which is incorporated herein byreference. In other implementations, the neural network-based templategenerator 1512 is a U-Net network with skip connections between thedecoder and the encoder between the decoder and the encoder, such as theone described in Ronneberger O, Fischer P, Brox T., “U-net:Convolutional networks for biomedical image segmentation,” Med. ImageComput. Comput. Assist. Interv. (2015), available at:http://link.springer.com/chapter/10.1007/978-3-319-24574-4 28, which isincorporated herein by reference. The U-Net architecture resembles anautoencoder with two main sub-structures: 1) an encoder, which takes aninput image and reduces its spatial resolution through multipleconvolutional layers to create a representation encoding. 2) A decoder,which takes the representation encoding and increases spatial resolutionback to produce a reconstructed image as output. The U-Net introducestwo innovations to this architecture: First, the objective function isset to reconstruct a segmentation mask using a loss function; andsecond, the convolutional layers of the encoder are connected to thecorresponding layers of the same resolution in the decoder using skipconnections. In yet further implementations, the neural network-basedtemplate generator 1512 is a deep fully convolutional segmentationneural network with an encoder subnetwork and a corresponding decodernetwork. In such an implementation, the encoder subnetwork includes ahierarchy of encoders and the decoder subnetwork includes a hierarchy ofdecoders that map low resolution encoder feature maps to full inputresolution feature maps. Additional details about segmentation networkscan be found in Appendix entitled “Segmentation Networks”.

In one implementation, the neural network-based template generator 1512is a convolutional neural network. In another implementation, the neuralnetwork-based template generator 1512 is a recurrent neural network. Inyet another implementation, the neural network-based template generator1512 is a residual neural network with residual bocks and residualconnections. In a further implementation, the neural network-basedtemplate generator 1512 is a combination of a convolutional neuralnetwork and a recurrent neural network.

One skilled in the art will appreciate that the neural network-basedtemplate generator 1512 (i.e., the neural network 1706 and/or the outputlayer 1710) can use various padding and striding configurations. It canuse different output functions (e.g., classification or regression) andmay or may not include one or more fully-connected layers. It can use 1Dconvolutions, 2D convolutions, 3D convolutions, 4D convolutions, 5Dconvolutions, dilated or atrous convolutions, transpose convolutions,depthwise separable convolutions, pointwise convolutions, 1×1convolutions, group convolutions, flattened convolutions, spatial andcross-channel convolutions, shuffled grouped convolutions, spatialseparable convolutions, and deconvolutions. It can use one or more lossfunctions such as logistic regression/log loss, multi-classcross-entropy/softmax loss, binary cross-entropy loss, mean-squarederror loss, L1 loss, L2 loss, smooth L1 loss, and Huber loss. It can useany parallelism, efficiency, and compression schemes such TFRecords,compressed encoding (e.g., PNG), sharding, parallel calls for maptransformation, batching, prefetching, model parallelism, dataparallelism, and synchronous/asynchronous SGD. It can include upsamplinglayers, downsampling layers, recurrent connections, gates and gatedmemory units (like an LSTM or GRU), residual blocks, residualconnections, highway connections, skip connections, peepholeconnections, activation functions (e.g., non-linear transformationfunctions like rectifying linear unit (ReLU), leaky ReLU, exponentialliner unit (ELU), sigmoid and hyperbolic tangent (tan h)), batchnormalization layers, regularization layers, dropout, pooling layers(e.g., max or average pooling), global average pooling layers, andattention mechanisms.

In some implementations, each image in the sequence of image sets 1602covers a tile and depicts intensity emissions of clusters on a tile andtheir surrounding background captured for a particular imaging channelat a particular one of a plurality of sequencing cycles of a sequencingrun performed on a flow cell. In one implementation, the input imagedata 1702 includes at least one image patch from each of the images inthe sequence of image sets 1602. In such an implementation, the imagepatch covers a portion of the tile. In one example, the image patch hasa resolution of 20×20. In other cases, the resolution of the image patchcan range from 20×20 to 10000×10000. In another implementation, theinput image data 1702 includes an upsampled, subpixel resolutionrepresentation of the image patch from each of the images in thesequence of image sets 1602. In one example, the upsampled, subpixelrepresentation has a resolution of 80×80. In other cases, the resolutionof the upsampled, subpixel representation can range from 80×80 to10000×10000.

The input image data 1702 has an array of units 1704 that depictsclusters and their surrounding background. For example, an image set canbe for a particular sequencing cycle and include four images, one foreach image channel A, C, T, and G. Then, for a sequencing run with fiftysequencing cycles, there will be fifty such image sets, i.e., a total of200 images. When arranged temporally, fifty image sets with fourimages-per image set would form the series of image sets 1602. In someimplementations, image patches of a certain size are extracted from eachimage in the fifty image sets, forming fifty image patch sets with fourimage patches-per image patch set and, in one implementation, this isthe input image data 1702. In other implementations, the input imagedata 1702 comprises image patch sets with four image patches-per imagepatch set for fewer than the fifty sequencing cycles, i.e., just one,two, three, fifteen, twenty sequencing cycles. The alternativerepresentation is a feature map. The feature map can be a convolvedfeature or convolved representation when the neural network is aconvolutional neural network. The feature map can be a hidden statefeature or hidden state representation when the neural network is arecurrent neural network.

Then, the technology disclosed processes the alternative representation1708 through an output layer 1710 to generate an output 1714 that has anoutput value 1712 for each unit in the array 1704. The output layer canbe a classification layer such as softmax or sigmoid that producesunit-wise output values. In one implementation, the output layer is aReLU layer or any other activation function layer that producesunit-wise output values.

In one implementation, the units in the input image data 1702 are pixelsand therefore pixel-wise output values 1712 are produced in the output1714. In another implementation, the units in the input image data 1702are subpixels and therefore subpixel-wise output values 1712 areproduced in the output 1714. In yet another implementation, the units inthe input image data 1702 are superpixels and therefore superpixel-wiseoutput values 1712 are produced in the output 1714.

Deriving Cluster Metadata from Decay Map, Ternary Map, and/or Binary Map

FIG. 18 shows one implementation of post-processing techniques that areapplied to the decay map 1716, the ternary map 1718, or the binary map1720 produced by the neural network-based template generator 1512 toderive cluster metadata, including cluster centers, cluster shapes,cluster sizes, cluster background, and/or cluster boundaries. In someimplementations, the post-processing techniques are applied by apost-processor 1814 that further comprises a thresholder 1802, a peaklocator 1806, and a segmenter 1810.

The input to the thresholder 1802 is the decay map 1716, the ternary map1718, or the binary map 1720 produced by template generator 1512, suchas the disclosed neural network-based template generator. In oneimplementation, the thresholder 1802 applies thresholding on the valuesin the decay map, the ternary map, or the binary map to identifybackground units 1804 (i.e., subpixels characterizing non-clusterbackground).) and non-background units. Said differently, once theoutput 1714 is produced, the thresholder 1802 thresholds output valuesof the units 1712 and classifies, or can reclassify a first subset ofthe units 1712 as “background units” 1804 depicting the surroundingbackground of the clusters and “non-background units” depicting unitsthat potentially belong to clusters. The threshold value applied by thethresholder 1802 can be preset.

The input to the peak locator 1806 is also the decay map 1716, theternary map 1718, or the binary map 1720 produced by the neuralnetwork-based template generator 1512. In one implementation, the peaklocator 1806 applies peak detection on the values in the decay map 1716,the ternary map 1718, or the binary map 1720 to identify center units1808 (i.e., center subpixels characterizing cluster centers). Saiddifferently, the peak locator 1806 processes the output values of theunits 1712 in the output 1714 and classifies a second subset of theunits 1712 as “center units” 1808 containing centers of the clusters. Insome implementations, the centers of the clusters detected by the peaklocator 1806 are also the centers of mass of the clusters. The centerunits 1808 are then provided to the segmenter 1810. Additional detailsabout the peak locator 1806 can be found in the Appendix entitled “PeakDetection”.

The thresholding and the peak detection can be done in parallel or oneafter the other. That is, they are not dependent on each other.

The input to the segmenter 1810 is also the decay map 1716, the ternarymap 1718, or the binary map 1720 produced by the neural network-basedtemplate generator 1512. Additional supplemental input to the segmenter1810 comprises the thresholded units (background, non-background) 1804identified by the thresholder 1802 and the center units 1808 identifiedby the peak locator 1806. The segmenter 1810 uses the background,non-background 1804 and the center units 1808 to identify disjointedregions 1812 (i.e., non-overlapping groups of contiguous cluster/clusterinterior subpixels characterizing clusters). Said differently, thesegmenter 1810 processes the output values of the units 1712 in theoutput 1714 and uses the background, non-background units 1804 and thecenter units 1808 to determine shapes 1812 of the clusters asnon-overlapping regions of contiguous units separated by the backgroundunits 1804 and centered at the center units 1808. The output of thesegmenter 1810 is cluster metadata 1812. The cluster metadata 1812identifies cluster centers, cluster shapes, cluster sizes, clusterbackground, and/or cluster boundaries.

In one implementation, the segmenter 1810 begins with the center units1808 and determines, for each center unit, a group of successivelycontiguous units that depict a same cluster whose center of mass iscontained in the center unit. In one implementation, the segmenter 1810uses a so-called “watershed” segmentation technique to subdividecontiguous clusters into multiple adjoining clusters at a valley inintensity. Additional details about the watershed segmentation techniqueand other segmentation techniques can be found in Appendix entitled“Watershed Segmentation”.

In one implementation, the output values of the units 1712 in the output1714 are continuous values, such as the one encoded in the ground truthdecay map 1204. In another implementation, the output values are softmaxscores, such as the one encoded in the ground truth ternary map 1304 andthe ground truth binary map 1404. In the ground truth decay map 1204,according to one implementation, the contiguous units in the respectiveones of the non-overlapping regions have output values weightedaccording to distance of a contiguous unit from a center unit in anon-overlapping region to which the contiguous unit belongs. In such animplementation, the center units have highest output values within therespective ones of the non-overlapping regions. As discussed above,during the training, due to the backward propagation-based gradientupdate, the decay map 1716, the ternary map 1718, and the binary map1720 (i.e., cumulatively the output 1714) progressively match orapproach the ground truth decay map 1204, the ground truth ternary map1304, and the ground truth binary map 1404, respectively.

Pixel Domain—Intensity Extraction from Irregular Cluster Shapes

The discussion now turns to how cluster shapes determined by thetechnology disclosed can be used to extract intensity of the clusters.Since clusters typically have irregular shapes and contours, thetechnology disclosed can be used to identify which subpixels contributeto the irregularly shaped disjointed/non-overlapping regions thatrepresent the cluster shapes.

FIG. 19 depicts one implementation of extracting cluster intensity inthe pixel domain. “Template image” or “template” can refer to a datastructure that contains or identifies the cluster metadata 1812 derivedfrom the decay map 1716, the ternary map 1718, and/or the binary map1718. The cluster metadata 1812 identifies cluster centers, clustershapes, cluster sizes, cluster background, and/or cluster boundaries.

In some implementations, the template image is in the upsampled,subpixel domain to distinguish the cluster boundaries at a fine-grainedlevel. However, the sequencing images 108, which contain the cluster andbackground intensity data, are typically in the pixel domain. Thus, thetechnology disclosed proposes two approaches to use the cluster shapeinformation encoded in the template image in the upsampled, subpixelresolution to extract intensities of the irregularly shaped clustersfrom the optical, pixel-resolution sequencing images. In the firstapproach, depicted in FIG. 19, the non-overlapping groups of contiguoussubpixels identified in the template image are located in the pixelresolution sequencing images and their intensities extracted viainterpolation. Additional details about this intensity extractiontechnique can be found in FIG. 33 and its discussion.

In one implementation, when the non-overlapping regions have irregularcontours and the units are subpixels, the cluster intensity 1912 of agiven cluster is determined by an intensity extractor 1902 as follows.

First, a subpixel locator 1904 identifies subpixels that contribute tothe cluster intensity of the given cluster based on a correspondingnon-overlapping region of contiguous subpixels that identifies a shapeof the given cluster.

Then, the subpixel locator 1904 locates the identified subpixels in oneor more optical, pixel-resolution images 1918 generated for one or moreimaging channels at a current sequencing cycle. In one implementation,integer or non-integer coordinates (e.g., floating points) are locatedin the optical, pixel-resolution images, after a downscaling based on adownscaling factor that matches an upsampling factor used to create thesubpixel domain.

Then, an interpolator and subpixel intensity combiner 1906, intensitiesof the identified subpixels in the images processed, combines theinterpolated intensities, and normalizes the combined interpolatedintensities to produce a per-image cluster intensity for the givencluster in each of the images. The normalization is performed by anormalizer 1908 and is based on a normalization factor. In oneimplementation, the normalization factor is a number of the identifiedsubpixels. This is done to normalize/account for different cluster sizesand uneven illuminations that clusters receive depending on theirlocation on the flow cell.

Finally, a cross-channel subpixel intensity accumulator 1910 combinesthe per-image cluster intensity for each of the images to determine thecluster intensity 1912 of the given cluster at the current sequencingcycle.

Then, the given cluster is base called based on the cluster intensity1912 at the current sequencing cycle by any one of the base callersdiscussed in this application, yielding base calls 1916.

In some implementations though, when the cluster sizes are large enough,the output of the neural network-based base caller 1514, i.e., the decaymap 1716, the ternary map 1718, and the binary map 1720 are in theoptical, pixel domain. Accordingly, in such implementations, thetemplate image is also in the optical, pixel domain.

Subpixel Domain—Intensity Extraction from Irregular Cluster Shapes

FIG. 20 depicts the second approach of extracting cluster intensity inthe subpixel domain. In this second approach, the sequencing images inthe optical, pixel-resolution are upsampled into the subpixelresolution. This results in correspondence between the “cluster shapedepicting subpixels” in the template image and the “cluster intensitydepicting subpixels” in the upsampled sequencing images. The clusterintensity is then extracted based on the correspondence. Additionaldetails about this intensity extraction technique can be found in FIG.33 and its discussion.

In one implementation, when the non-overlapping regions have irregularcontours and the units are subpixels, the cluster intensity 2012 of agiven cluster is determined by an intensity extractor 2002 as follows.

First, a subpixel locator 2004 identifies subpixels that contribute tothe cluster intensity of the given cluster based on a correspondingnon-overlapping region of contiguous subpixels that identifies a shapeof the given cluster.

Then, the subpixel locator 2004 locates the identified subpixels in oneor more subpixel resolution images 2018 upsampled from correspondingoptical, pixel-resolution images 1918 generated for one or more imagingchannels at a current sequencing cycle. The upsampling can be performedby nearest neighbor intensity extraction, Gaussian based intensityextraction, intensity extraction based on average of 2×2 subpixel area,intensity extraction based on brightest of 2×2 subpixel area, intensityextraction based on average of 3×3 subpixel area, bilinear intensityextraction, bicubic intensity extraction, and/or intensity extractionbased on weighted area coverage. These techniques are described indetail in Appendix entitled “Intensity Extraction Methods”. The templateimage can, in some implementations, serve as a mask for intensityextraction.

Then, a subpixel intensity combiner 2006, in each of the upsampledimages, combines intensities of the identified subpixels and normalizesthe combined intensities to produce a per-image cluster intensity forthe given cluster in each of the upsampled images. The normalization isperformed by a normalizer 2008 and is based on a normalization factor.In one implementation, the normalization factor is a number of theidentified subpixels. This is done to normalize/account for differentcluster sizes and uneven illuminations that clusters receive dependingon their location on the flow cell.

Finally, a cross-channel, subpixel-intensity accumulator 2010 combinesthe per-image cluster intensity for each of the upsampled images todetermine the cluster intensity 2012 of the given cluster at the currentsequencing cycle.

Then, the given cluster is base called based on the cluster intensity2012 at the current sequencing cycle by any one of the base callersdiscussed in this application, yielding base calls 2016.

Types of Neural Network-Based Template Generators

The discussion now turns to details of three different implementationsof the neural network-based template generator 1512. There are shown inFIG. 21a and include: (1) the decay map-based template generator 2600(also called the regression model), (2) the binary map-based templategenerator 4600 (also called the binary classification model), and (3)the ternary map-based template generator 5400 (also called the ternaryclassification model).

In one implementation, the regression model 2600 is a fullyconvolutional network. In another implementation, the regression model2600 is a U-Net network with skip connections between the decoder andthe encoder. In one implementation, the binary classification model 4600is a fully convolutional network. In another implementation, the binaryclassification model 4600 is a U-Net network with skip connectionsbetween the decoder and the encoder. In one implementation, the ternaryclassification model 5400 is a fully convolutional network. In anotherimplementation, the ternary classification model 5400 is a U-Net networkwith skip connections between the decoder and the encoder.

Input Image Data

FIG. 21b depicts one implementation of the input image data 1702 that isfed as input to the neural network-based template generator 1512. Theinput image data 1702 comprises a series of image sets 2100 with thesequencing images 108 that are generated during a certain number ofinitial sequences cycles of a sequencing run (e.g., the first 2 to 7sequencing cycles).

In some implementations, intensities of the sequencing images 108 arecorrected for background and/or aligned with each other using affinetransformation. In one implementation, the sequencing run utilizesfour-channel chemistry and each image set has four images. In anotherimplementation, the sequencing run utilizes two-channel chemistry andeach image set has two images. In yet another implementation, thesequencing run utilizes one-channel chemistry and each image set has twoimages. In yet other implementations, each image set has only one image.These and other different implementations are described in Appendices 6and 9.

Each image 2116 in the series of image sets 2100 covers a tile 2104 of aflow cell 2102 and depicts intensity emissions of clusters 2106 on thetile 2104 and their surrounding background captured for a particularimage channel at a particular one of a plurality of sequencing cycles ofthe sequencing run. In one example, for cycle tl, the image set includesfour images 2112A, 2112C, 2112T, and 2112G: one image for each base A,C, T, and G labeled with a corresponding fluorescent dye and imaged in acorresponding wavelength band (image/imaging channel).

For illustration purposes, in image 2112G, FIG. 21b depicts clusterintensity emissions as 2108 and background intensity emissions as 2110.In another example, for cycle tn, the image set also includes fourimages 2114A, 2114C, 2114T, and 2114G: one image for each base A, C, T,and G labeled with a corresponding fluorescent dye and imaged in acorresponding wavelength band (image/imaging channel). Also forillustration purposes, in image 2114A, FIG. 21b depicts clusterintensity emissions as 2118 and, in image 2114T, depicts backgroundintensity emissions as 2120.

The input image data 1702 is encoded using intensity channels (alsocalled imaged channels). For each of the c images obtained from thesequencer for a particular sequencing cycle, a separate imaged channelis used to encode its intensity signal data. Consider, for example, thatthe sequencing run uses the 2-channel chemistry which produces a redimage and a green image at each sequencing cycle. In such a case, theinput data 2632 comprises (i) a first red imaged channel with w×h pixelsthat depict intensity emissions of the one or more clusters and theirsurrounding background captured in the red image and (ii) a second greenimaged channel with w×h pixels that depict intensity emissions of theone or more clusters and their surrounding background captured in thegreen image.

Non-Image Data

In another implementation, the input data to the neural network-basedtemplate generator 1512 and the neural network-based base caller 1514 isbased on pH changes induced by the release of hydrogen ions duringmolecule extension. The pH changes are detected and converted to avoltage change that is proportional to the number of bases incorporated(e.g., in the case of Ion Torrent).

In yet another implementation, the input data is constructed fromnanopore sensing that uses biosensors to measure the disruption incurrent as an analyte passes through a nanopore or near its aperturewhile determining the identity of the base. For example, the OxfordNanopore Technologies (ONT) sequencing is based on the followingconcept: pass a single strand of DNA (or RNA) through a membrane via ananopore and apply a voltage difference across the membrane. Thenucleotides present in the pore will affect the pore's electricalresistance, so current measurements over time can indicate the sequenceof DNA bases passing through the pore. This electrical current signal(the ‘squiggle’ due to its appearance when plotted) is the raw datagathered by an ONT sequencer. These measurements are stored as 16-bitinteger data acquisition (DAC) values, taken at 4 kHz frequency (forexample). With a DNA strand velocity of −450 base pairs per second, thisgives approximately nine raw observations per base on average. Thissignal is then processed to identify breaks in the open pore signalcorresponding to individual reads. These stretches of raw signal arebase called—the process of converting DAC values into a sequence of DNAbases. In some implementations, the input data comprises normalized orscaled DAC values.

In another implementation, image data is not used as input to the neuralnetwork-based template generator 1512 or the neural network-based basecaller 1514. Instead, the input to the neural network-based templategenerator 1512 and the neural network-based base caller 1514 is based onpH changes induced by the release of hydrogen ions during moleculeextension. The pH changes are detected and converted to a voltage changethat is proportional to the number of bases incorporated (e.g., in thecase of Ion Torrent).

In yet another implementation, the input to the neural network-basedtemplate generator 1512 and the neural network-based base caller 1514 isconstructed from nanopore sensing that uses biosensors to measure thedisruption in current as an analyte passes through a nanopore or nearits aperture while determining the identity of the base. For example,the Oxford Nanopore Technologies (ONT) sequencing is based on thefollowing concept: pass a single strand of DNA (or RNA) through amembrane via a nanopore and apply a voltage difference across themembrane. The nucleotides present in the pore will affect the pore'selectrical resistance, so current measurements over time can indicatethe sequence of DNA bases passing through the pore. This electricalcurrent signal (the ‘squiggle’ due to its appearance when plotted) isthe raw data gathered by an ONT sequencer. These measurements are storedas 16-bit integer data acquisition (DAC) values, taken at 4 kHzfrequency (for example). With a DNA strand velocity of −450 base pairsper second, this gives approximately nine raw observations per base onaverage. This signal is then processed to identify breaks in the openpore signal corresponding to individual reads. These stretches of rawsignal are base called—the process of converting DAC values into asequence of DNA bases. In some implementations, the input data 2632comprises normalized or scaled DAC values.

Patch Extraction

FIG. 22 shows one implementation of extracting patches from the seriesof image sets 2100 in FIG. 21b to produce a series of “down-sized” imagesets that form the input image data 1702. In the illustratedimplementation, the sequencing images 108 in the series of image sets2100 are of size L×L (e.g., 2000×2000). In other implementations, L isany number ranging from 1 and 10,000.

In one implementation, a patch extractor 2202 extracts patches from thesequencing images 108 in the series of image sets 2100 and produces aseries of down-sized image sets 2206, 2208, 2210, and 2212. Each imagein the series of down-sized image sets is a patch of size M×M (e.g.,20×20) that is extracted from a corresponding sequencing image in theseries of image sets 2100. The size of the patches can be preset.

In other implementations, M is any number ranging from 1 and 1000.

In FIG. 22, four example series of down-sized image sets are shown. Thefirst example series of down-sized image sets 2206 is extracted fromcoordinates 0,0 to 20,20 in the sequencing images 108 in the series ofimage sets 2100. The second example series of down-sized image sets 2208is extracted from coordinates 20,20 to 40,40 in the sequencing images108 in the series of image sets 2100. The third example series ofdown-sized image sets 2210 is extracted from coordinates 40,40 to 60,60in the sequencing images 108 in the series of image sets 2100. Thefourth example series of down-sized image sets 2212 is extracted fromcoordinates 60,60 to 80,80 in the sequencing images 108 in the series ofimage sets 2100.

In some implementations, the series of down-sized image sets form theinput image data 1702 that is fed as input to the neural network-basedtemplate generator 1512. Multiple series of down-sized image sets can besimultaneously fed as an input batch and a separate output can beproduced for each series in the input batch.

Upsampling

FIG. 23 depicts one implementation of upsampling the series of imagesets 2100 in FIG. 21b to produce a series of “upsampled” image sets 2300that forms the input image data 1702.

In one implementation, an upsampler 2302 uses interpolation (e.g.,bicubic interpolation) to upsample the sequencing images 108 in theseries of image sets 2100 by an upsampling factor (e.g., 4×) and theseries of upsampled image sets 2300.

In the illustrated implementation, the sequencing images 108 in theseries of image sets 2100 are of size L×L (e.g., 2000×2000) and areupsampled by an upsampling factor of four to produce upsampled images ofsize U×U (e.g., 8000×8000) in the series of upsampled image sets 2300.

In one implementation, the sequencing images 108 in the series of imagesets 2100 are fed directly to the neural network-based templategenerator 1512 and the upsampling is performed by an initial layer ofthe neural network-based template generator 1512. That is, the upsampler2302 is part of the neural network-based template generator 1512 andoperates as its first layer that upsamples the sequencing images 108 inthe series of image sets 2100 and produces the series of upsampled imagesets 2300.

In some implementations, the series of upsampled image sets 2300 formsthe input image data 1702 that is fed as input to the neuralnetwork-based template generator 1512.

FIG. 24 shows one implementation of extracting patches from the seriesof upsampled image sets 2300 in FIG. 23 to produce a series of“upsampled and down-sized” image sets 2406, 2408, 2410, and 2412 thatform the input image data 1702.

In one implementation, the patch extractor 2202 extracts patches fromthe upsampled images in the series of upsampled image sets 2300 andproduces series of upsampled and down-sized image sets 2406, 2408, 2410,and 2412. Each upsampled image in the series of upsampled and down-sizedimage sets is a patch of size M×M (e.g., 80×80) that is extracted from acorresponding upsampled image in the series of upsampled image sets2300. The size of the patches can be preset. In other implementations, Mis any number ranging from 1 and 1000.

In FIG. 24, four example series of upsampled and down-sized image setsare shown. The first example series of upsampled and down-sized imagesets 2406 is extracted from coordinates 0,0 to 80,80 in the upsampledimages in the series of upsampled image sets 2300. The second exampleseries of upsampled and down-sized image sets 2408 is extracted fromcoordinates 80,80 to 160,160 in the upsampled images in the series ofupsampled image sets 2300. The third example series of upsampled anddown-sized image sets 2410 is extracted from coordinates 160,160 to240,240 in the upsampled images in the series of upsampled image sets2300. The fourth example series of upsampled and down-sized image sets2412 is extracted from coordinates 240,240 to 320,320 in the upsampledimages in the series of upsampled image sets 2300.

In some implementations, the series of upsampled and down-sized imagesets form the input image data 1702 that is fed as input to the neuralnetwork-based template generator 1512. Multiple series of upsampled anddown-sized image sets can be simultaneously fed as an input batch and aseparate output can be produced for each series in the input batch.

Output

The three models are trained to produce different outputs. This isachieved by using different types of ground truth data representationsas training labels. The regression model 2600 is trained to produceoutput that characterizes/represents/denotes a so-called “decay map”1716. The binary classification model 4600 is trained to produce outputthat characterizes/represents/denotes a so-called “binary map” 1720. Theternary classification model 5400 is trained to produce output thatcharacterizes/represents/denotes a so-called “ternary map” 1718.

The output 1714 of each type of model comprises an array of units 1712.The units 1712 can be pixels, subpixels, or superpixels. The output ofeach type of model includes unit-wise output values, such that theoutput values of an array of units togethercharacterize/represent/denote the decay map 1716 in the case of theregression model 2600, the binary map 1720 in the case of the binaryclassification model 4600, and the ternary map 1718 in the case of theternary classification model 5400. More details follow.

Ground Truth Data Generation

FIG. 25 illustrates one implementation of an overall example process ofgenerating ground truth data for training the neural network-basedtemplate generator 1512. For the regression model 2600, the ground truthdata can be the decay map 1204. For the binary classification model4600, the ground truth data can be the binary map 1404. For the ternaryclassification model 5400, the ground truth data can be the ternary map1304. The ground truth data is generated from the cluster metadata. Thecluster metadata is generated by the cluster metadata generator 122. Theground truth data is generated by the ground truth data generator 1506.

In the illustrated implementation, the ground truth data is generatedfor tile A that is on lane A of flow cell A. The ground truth data isgenerated from the sequencing images 108 of tile A captured duringsequencing run A. The sequencing images 108 of tile A are in the pixeldomain. In one example involving 4-channel chemistry that generates foursequencing images per sequencing cycle, two hundred sequencing images108 for fifty sequencing cycles are accessed. Each of the two hundredsequencing images 108 depicts intensity emissions of clusters on tile Aand their surrounding background captured in a particular image channelat a particular sequencing cycle.

The subpixel addresser 110 converts the sequencing images 108 into thesubpixel domain (e.g., by dividing each pixel into a plurality ofsubpixels) and produces sequencing images 112 in the subpixel domain.

The base caller 114 (e.g., RTA) then processes the sequencing images 112in the subpixel domain and produces a base call for each subpixel andfor each of the fifty sequencing cycles. This is referred to herein as“subpixel base calling”.

The subpixel base calls 116 are then merged to produce, for eachsubpixel, a base call sequence across the fifty sequencing cycles. Eachsubpixel's base call sequence has fifty base calls, i.e., one base callfor each of the fifty sequencing cycles.

The searcher 118 evaluates base call sequences of contiguous subpixelson a pair-wise basis. The search involves evaluating each subpixel todetermine with which of its contiguous subpixels it shares asubstantially matching base call sequence. Base call sequences ofcontiguous subpixels are “substantially matching” when a predeterminedportion of base calls match on an ordinal position-wise basis(e.g., >=41 matches in 45 cycles, <=4 mismatches in 45 cycles, <=4mismatches in 50 cycles, or <=2 mismatches in 34 cycles).

In some implementations, the base caller 114 also identifies preliminarycenter coordinates of the clusters. Subpixels that contain thepreliminary center coordinates are referred to as center or originsubpixels. Some example preliminary center coordinates (604 a-c)identified by the base caller 114 and corresponding origin subpixels(606 a-c) are shown in FIG. 6. However, identification of the originsubpixels (preliminary center coordinates of the clusters) is notneeded, as explained below. In some implementations, the searcher 118uses a breadth-first search for identifying substantially matching basecall sequences of the subpixels by beginning with the origin subpixels606 a-c and continuing with successively contiguous non-origin subpixels702 a-c. This again is optional, as explained below.

The search for substantially matching base call sequences of thesubpixels does not need identification of the origin subpixels(preliminary center coordinates of the clusters) because the search canbe done for all the subpixels and the search does not have to start fromthe origin subpixels and instead can start from any subpixel (e.g., 0,0subpixel or any random subpixel). Thus, since each subpixel is evaluatedto determine whether it shares a substantially matching base callsequence with another contiguous subpixel, the search does not have toutilize the origin subpixels and can start with any subpixel.

Irrespective of whether origin subpixels are used or not, certainclusters are identified that do not contain the origin subpixels(preliminary center coordinates of the clusters) predicted by the basecaller 114. Some examples of clusters identified by the merging of thesubpixel base calls and not containing an origin subpixel are clusters812 a, 812 b, 812 c, 812 d, and 812 e in FIG. 8a . Therefore, use of thebase caller 114 for identification of origin subpixels (preliminarycenter coordinates of the clusters) is optional and not essential forthe search of substantially matching base call sequences of thesubpixels.

The searcher 118: (1) identifies contiguous subpixels with substantiallymatching base call sequences as so-called “disjointed regions”, (2)further evaluates base call sequences of those subpixels that do notbelong to any of the disjointed regions already identified at (1) toyield additional disjointed regions, and (3) then identifies backgroundsubpixels as those subpixels that do not belong to any of the disjointedregions already identified at (1) and (2). Action (2) allows thetechnology disclosed to identify additional or extra clusters for whichthe centers are not identified by the base caller 114.

The results of the searcher 118 are encoded in a so-called “cluster map”of tile A and stored in the cluster map data store 120. In the clustermap, each of the clusters on tile A are identified by a respectivedisjointed region of contiguous subpixels, with background subpixelsseparating the disjointed regions to identify the surrounding backgroundon tile A.

The center of mass (COM) calculator 1004 determines a center for each ofthe clusters on tile A by calculating a COM of each of the disjointedregions as an average of coordinates of respective contiguous subpixelsforming the disjointed regions. The centers of mass of the clusters arestored as COM data 2502.

A subpixel categorizer 2504 uses the cluster map and the COM data 2502to produce subpixel categorizations 2506. The subpixel categorizations2506 classify subpixels in the cluster map as (1) backgrounds subpixels,(2) COM subpixels (one COM subpixel for each disjointed regioncontaining the COM of the respective disjointed region), and (3)cluster/cluster interior subpixels forming the respective disjointedregions. That is, each subpixel in the cluster map is assigned one ofthe three categories.

Based on the subpixel categorizations 2506, in some implementations, (i)the ground truth decay map 1204 is produced by the ground truth decaymap generator 1202, (ii) the ground truth binary map 1304 is produced bythe ground truth binary map generator 1302, and (iii) the ground truthternary map 1404 is produced by the ground truth ternary map generator1402.

1. Regression Model

FIG. 26 illustrates one implementation of the regression model 2600. Inthe illustrated implementation, the regression model 2600 is a fullyconvolutional network 2602 that processes the input image data 1702through an encoder subnetwork and a corresponding decoder subnetwork.The encoder subnetwork includes a hierarchy of encoders. The decodersubnetwork includes a hierarchy of decoders that map low resolutionencoder feature maps to a full input resolution decay map 1716. Inanother implementation, the regression model 2600 is a U-Net network2604 with skip connections between the decoder and the encoder.Additional details about the segmentation networks can be found in theAppendix entitled “Segmentation Networks”.

Decay Map

FIG. 27 depicts one implementation of generating a ground truth decaymap 1204 from a cluster map 2702. The ground truth decay map 1204 isused as ground truth data for training the regression model 2600. In theground truth decay map 1204, the ground truth decay map generator 1202assigns a weighted decay value to each contiguous subpixel in thedisjointed regions based on a weighted decay factor. The weighted decayvalue is proportional to Euclidean distance of a contiguous subpixelfrom a center of mass (COM) subpixel in a disjointed region to which thecontiguous subpixel belongs, such that the weighted decay value ishighest (e.g., 1 or 100) for the COM subpixel and decreases forsubpixels further away from the COM subpixel. In some implementations,the weighted decay value is multiplied by a preset factor, such as 100.

Further, the ground truth decay map generator 1202 assigns allbackground subpixels a same predetermine value (e g., a minimalistbackground value).

The ground truth decay map 1204 expresses the contiguous subpixels inthe disjointed regions and the background subpixels based on theassigned values. The ground truth decay map 1204 also stores theassigned values in an array of units, with each unit in the arrayrepresenting a corresponding subpixel in the input.

Training

FIG. 28 is one implementation of training 2800 the regression model 2600using a backpropagation-based gradient update technique that modifiesparameters of the regression model 2600 until the decay map 1716produced by the regression model 2600 as training output during thetraining 2800 progressively approaches or matches the ground truth decaymap 1204.

The training 2800 includes iteratively optimizing a loss function thatminimizes error 2806 between the decay map 1716 and the ground truthdecay map 1204, and updating parameters of the regression model 2600based on the error 2806. In one implementation, the loss function ismean squared error and the error is minimized on a subpixel-by-subpixelbasis between weighted decay values of corresponding subpixels in thedecay map 1716 and the ground truth decay map 1204.

The training 2800 includes hundreds, thousands, and/or millions ofiterations of forward propagation 2808 and backward propagation 2810,including parallelization techniques such as batching. The training data1504 includes, as the input image data 1702, a series of upsampled anddown-sized image sets. The training data 1504 is annotated with groundtruth labels by an annotator 2806. The training 2800 is operationalizedby the trainer 1510 using a stochastic gradient update algorithm such asADAM.

Inference

FIG. 29 is one implementation of template generation by the regressionmodel 2600 during inference 2900 in which the decay map 1716 is producedby the regression model 2600 as the inference output during theinference 2900. One example of the decay map 1716 is disclosed in theAppendix titled “Regression_Model_Sample_Ouput”. The Appendix includesunit-wise weighted decay output values 2910 that together represent thedecay map 1716.

The inference 2900 includes hundreds, thousands, and/or millions ofiterations of forward propagation 2904, including parallelizationtechniques such as batching. The inference 2900 is performed oninference data 2908 that includes, as the input image data 1702, aseries of upsampled and down-sized image sets. The inference 2900 isoperationalized by a tester 2906.

Watershed Segmentation

FIG. 30 illustrates one implementation of subjecting the decay map 1716to (i) thresholding to identify background subpixels characterizingcluster background and to (ii) peak detection to identify centersubpixels characterizing cluster centers. The thresholding is performedby the thresholder 1802 that uses a local threshold binary to producebinarized output. The peak detection is performed by the peak locator1806 to identify the cluster centers. Additional details about the peaklocator can be found in the Appendix entitled “Peak Detection”.

FIG. 31 depicts one implementation of a watershed segmentation techniquethat takes as input the background subpixels and the center subpixelsrespectively identified by the thresholder 1802 and the peak locator1806, finds valleys in intensity between adjoining clusters, and outputsnon-overlapping groups of contiguous cluster/cluster interior subpixelscharacterizing the clusters. Additional details about the watershedsegmentation technique can be found in the Appendix entitled “WatershedSegmentation”.

In one implementation, a watershed segmenter 3102 takes as input (1)negativized output values 2910 in the decay map 1716, (2) binarizedoutput of the thresholder 1802, and (3) cluster centers identified bythe peak locator 1806. Then, based on the input, the watershed segmenter3102 produces output 3104. In the output 3104, each cluster center isidentified as a unique set/group of subpixels that belong to the clustercenter (as long as the subpixels are “1” in the binary output, i.e., notbackground subpixels). Further, the clusters are filtered based oncontaining at least four subpixels. The watershed segmenter 3102 can bepart of the segmenter 1810, which in turn is part of the post-processor1814.

Network Architecture

FIG. 32 is a table that shows an example U-Net architecture of theregression model 2600, along with details of the layers of theregression model 2600, dimensionality of the output of the layers,magnitude of the model parameters, and interconnections between thelayers. Similar details are disclosed in the file titled “RegressionModel Example Architecture”, which is submitted as an appendix to thisapplication.

Cluster Intensity Extraction

FIG. 33 illustrates different approaches of extracting cluster intensityusing cluster shape information identified in a template image. Asdiscussed above, the template image identifies the cluster shapeinformation in the upsampled, subpixel resolution. However, the clusterintensity information is in the sequencing images 108, which aretypically in the optical, pixel-resolution.

According to a first approach, coordinates of the subpixels are locatedin the sequencing images 108 and their respective intensities extractedusing bilinear interpolation and normalized based on a count of thesubpixels that contribute to a cluster.

The second approach uses a weighted area coverage technique to modulatethe intensity of a pixel according to a number of subpixels thatcontribute to the pixel. Here too, the modulated pixel intensity isnormalized by a subpixel count parameter.

The third approach upsamples the sequencing images into the subpixeldomain using bicubic interpolation, sums the intensity of the upsampledpixels belonging to a cluster, and normalizes the summed intensity basedon a count of the upsampled pixels that belong to the cluster.

Experimental Results and Observations

FIG. 34 shows different approaches of base calling using the outputs ofthe regression model 2600. In the first approach, the cluster centersidentified from the output of the neural network-based templategenerator 1512 in the template image are fed to a base caller (e.g.,Illumina's Real-Time Analysis software, referred to herein as “RTA basecaller”) for base calling.

In the second approach, instead of the cluster centers, the clusterintensities extracted from the sequencing images based on the clustershape information in the template image are fed to the RTA base callerfor base calling.

FIG. 35 illustrates the difference in base calling performance when theRTA base caller uses ground truth center of mass (COM) location as thecluster center, as opposed to using a non-COM location as the clustercenter. The results show that using COM improves base calling.

Example Model Outputs

FIG. 36 shows, on the left, an example decay map 1716 produced by theregression model 2600. On the right, FIG. 36 also shows an exampleground truth decay map 1204 that the regression model 2600 approximatesduring the training.

Both the decay map 1716 and the ground truth decay map 1204 depictclusters as disjointed regions of contiguous subpixels, the centers ofthe clusters as center subpixels at centers of mass of the respectiveones of the disjointed regions, and their surrounding background asbackground subpixels not belonging to any of the disjointed regions.

Also, the contiguous subpixels in the respective ones of the disjointedregions have values weighted according to distance of a contiguoussubpixel from a center subpixel in a disjointed region to which thecontiguous subpixel belongs. In one implementation, the center subpixelshave the highest values within the respective ones of the disjointedregions. In one implementation, the background subpixels all have a sameminimalist background value within a decay map.

FIG. 37 portrays one implementation of the peak locator 1806 identifyingcluster centers in a decay map by detecting peaks 3702. Additionaldetails about the peak locator can be found in the Appendix entitled“Peak Detection”.

FIG. 38 compares peaks detected by the peak locator 1806 in the decaymap 1716 produced by the regression model 2600 with peaks in acorresponding ground truth decay map 1204. The red markers are peakspredicted by the regression model 2600 as cluster centers and the greenmarkers are the ground truth centers of mass of the clusters.

More Experimental Results and Observations

FIG. 39 illustrates performance of the regression model 2600 usingprecision and recall statistics. The precision and recall statisticsdemonstrate that the regression model 2600 is good at recovering allidentified cluster centers.

FIG. 40 compares performance of the regression model 2600 with the RTAbase caller for 20 pM library concentration (normal run). Outperformingthe RTA base caller, the regression model 2600 identifies 34, 323(4.46%) more clusters in a higher cluster density environment (i.e.,988,884 clusters).

FIG. 40 also shows results for other sequencing metrics such as numberof clusters that pass the chastity filter (“% PF” (pass-filter)), numberof aligned reads (“% Aligned”), number of duplicate reads (“%Duplicate”), number of reads mismatching the reference sequence for allreads aligned to the reference sequence (“% Mismatch”), bases calledwith quality score 30 and above (“% Q30 bases”), and so on.

FIG. 41 compares performance of the regression model 2600 with the RTAbase caller for 30 pM library concentration (dense run). Outperformingthe RTA base caller, the regression model 2600 identifies 34, 323(6.27%) more clusters in a much higher cluster density environment(i.e., 1,351,588 clusters).

FIG. 41 also shows results for other sequencing metrics such as numberof clusters that pass the chastity filter (“% PF” (pass-filter)), numberof aligned reads (“% Aligned”), number of duplicate reads (“%Duplicate”), number of reads mismatching the reference sequence for allreads aligned to the reference sequence (“% Mismatch”), bases calledwith quality score 30 and above (“% Q30 bases”), and so on.

FIG. 42 compares number of non-duplicate (unique or deduplicated) properread pairs, i.e., the number of paired reads that have both readsaligned inwards within a reasonable distance detected by the regressionmodel 2600 versus the same detected by the RTA base caller. Thecomparison is made both for the 20 pM normal run and the 30 pM denserun.

More importantly, FIG. 42 shows that the disclosed neural network-basedtemplate generators are able to detect more clusters in fewer sequencingcycles of input to template generation than the RTA base caller. In justfour sequencing cycles, the regression model 2600 identifies 11% morenon-duplicate proper read pairs than the RTA base caller during the 20pM normal run and 33% more non-duplicate proper read pairs than the RTAbase caller during the 30 pM dense run. In just seven sequencing cycles,the regression model 2600 identifies 4.5% more non-duplicate proper readpairs than the RTA base caller during the 20 pM normal run and 6.3% morenon-duplicate proper read pairs than the RTA base caller during the 30pM dense run.

FIG. 43 shows, on the right, a first decay map produced by theregression model 2600. The first decay map identifies clusters and theirsurrounding background imaged during the 20 pM normal run, along withtheir spatial distribution depicting cluster shapes, cluster sizes, andcluster centers.

On the left, FIG. 43 shows a second decay map produced by the regressionmodel 2600. The second decay map identifies clusters and theirsurrounding background imaged during the 30 pM dense run, along withtheir spatial distribution depicting cluster shapes, cluster sizes, andcluster centers.

FIG. 44 compares performance of the regression model 2600 with the RTAbase caller for 40 pM library concentration (highly dense run). Theregression model 2600 produced 89,441,688 more aligned bases than theRTA base caller in a much higher cluster density environment (i.e.,1,509,395 clusters).

FIG. 44 also shows results for other sequencing metrics such as numberof clusters that pass the chastity filter (“% PF” (pass-filter)), numberof aligned reads (“% Aligned”), number of duplicate reads (“%Duplicate”), number of reads mismatching the reference sequence for allreads aligned to the reference sequence (“% Mismatch”), bases calledwith a quality score 30 and above (“% Q30 bases”), and so on.

More Example Model Outputs

FIG. 45 shows, on the left, a first decay map produced by the regressionmodel 2600. The first decay map identifies clusters and theirsurrounding background imaged during the 40 pM normal run, along withtheir spatial distribution depicting cluster shapes, cluster sizes, andcluster centers.

On the right, FIG. 45 shows the results of the thresholding and the peaklocating applied to the first decay map to distinguish the respectiveclusters from each other and from the background and to identify theirrespective cluster centers. In some implementations, intensities of therespective clusters are identified and a chastity filter (or passingfilter) applied to reduce the mismatch rate.

2. Binary Classification Model

FIG. 46 illustrates one implementation of the binary classificationmodel 4600. In the illustrated implementation, the binary classificationmodel 4600 is a deep fully convolutional segmentation neural networkthat processes the input image data 1702 through an encoder subnetworkand a corresponding decoder subnetwork. The encoder subnetwork includesa hierarchy of encoders. The decoder subnetwork includes a hierarchy ofdecoders that map low resolution encoder feature maps to a full inputresolution binary map 1720. In another implementation, the binaryclassification model 4600 is a U-Net network with skip connectionsbetween the decoder and the encoder. Additional details about thesegmentation networks can be found in the Appendix entitled“Segmentation Networks”.

Binary Map

The final output layer of the binary classification model 4600 is aunit-wise classification layer that produces a classification label foreach unit in an output array. In some implementations, the unit-wiseclassification layer is a subpixel-wise classification layer thatproduces a softmax classification score distribution for each subpixelin the binary map 1720 across two classes, namely, a cluster centerclass and a non-cluster class, and the classification label for a givensubpixel is determined from the corresponding softmax classificationscore distribution.

In other implementations, the unit-wise classification layer is asubpixel-wise classification layer that produces a sigmoidclassification score for each subpixel in the binary map 1720, such thatthe activation of a unit is interpreted as the probability that the unitbelongs to the first class and, conversely, one minus the activationgives the probability that it belongs to the second class.

The binary map 1720 expresses each subpixel based on the predictedclassification scores. The binary map 1720 also stores the predictedvalue classification scores in an array of units, with each unit in thearray representing a corresponding subpixel in the input.

Training

FIG. 47 is one implementation of training 4700 the binary classificationmodel 4600 using a backpropagation-based gradient update technique thatmodifies parameters of the binary classification model 4600 until thebinary map 1720 of the binary classification model 4600 progressivelyapproaches or matches the ground truth binary map 1404.

In the illustrated implementation, the final output layer of the binaryclassification model 4600 is a softmax-based subpixel-wiseclassification layer. In softmax implementations, the ground truthbinary map generator 1402 assigns each ground truth subpixel either (i)a cluster center value pair (e.g., [1, 0]) or (ii) a non-center valuepair (e.g., [0, 1]).

In the cluster center value pair [1, 0], a first value [1] representsthe cluster center class label and a second value [0] represents thenon-center class label. In the non-center value pair [0, 1], a firstvalue [0] represents the cluster center class label and a second value[1] represents the non-center class label.

The ground truth binary map 1404 expresses each subpixel based on theassigned value pair/value. The ground truth binary map 1404 also storesthe assigned value pairs/values in an array of units, with each unit inthe array representing a corresponding subpixel in the input.

The training includes iteratively optimizing a loss function thatminimizes error 4706 (e.g., softmax error) between the binary map 1720and the ground truth binary map 1404, and updating parameters of thebinary classification model 4600 based on the error 4706.

In one implementation, the loss function is a custom-weighted binarycross-entropy loss and the error 4706 is minimized on asubpixel-by-subpixel basis between predicted classification scores(e.g., softmax scores) and labelled class scores (e.g., softmax scores)of corresponding subpixels in the binary map 1720 and the ground truthbinary map 1404, as shown in FIG. 47.

The custom-weighted loss function gives more weight to the COMsubpixels, such that the cross-entropy loss is multiplied by acorresponding reward (or penalty) weight specified in a reward (orpenalty) matrix whenever a COM subpixel is misclassified. Additionaldetails about the custom-weighted loss function can be found in theAppendix entitled “Custom-Weighted Loss Function”.

The training 4700 includes hundreds, thousands, and/or millions ofiterations of forward propagation 4708 and backward propagation 4710,including parallelization techniques such as batching. The training data1504 includes, as the input image data 1702, a series of upsampled anddown-sized image sets. The training data 1504 is annotated with groundtruth labels by the annotator 2806. The training 2800 is operationalizedby the trainer 1510 using a stochastic gradient update algorithm such asADAM.

FIG. 48 is another implementation of training 4800 the binaryclassification model 4600, in which the final output layer of the binaryclassification model 4600 is a sigmoid-based subpixel-wiseclassification layer.

In sigmoid implementations, the ground truth binary map generator 1302assigns each ground truth subpixel either (i) a cluster center value(e.g., [1]) or (ii) a non-center value (e.g., [0]). The COM subpixelsare assigned the cluster center value pair/value and all other subpixelsare assigned the non-center value pair/value.

With the cluster center value, values above a threshold intermediatevalue between 0 and 1 (e.g., values above 0.5) represent the centerclass label. With the non-center value, values below a thresholdintermediate value between 0 and 1 (e.g., values below 0.5) representthe non-center class label.

The ground truth binary map 1404 expresses each subpixel based on theassigned value pair/value. The ground truth binary map 1404 also storesthe assigned value pairs/values in an array of units, with each unit inthe array representing a corresponding subpixel in the input.

The training includes iteratively optimizing a loss function thatminimizes error 4806 (e.g., sigmoid error) between the binary map 1720and the ground truth binary map 1404, and updating parameters of thebinary classification model 4600 based on the error 4806.

In one implementation, the loss function is a custom-weighted binarycross-entropy loss and the error 4806 is minimized on asubpixel-by-subpixel basis between predicted scores (e.g., sigmoidscores) and labelled scores (e.g., sigmoid scores) of correspondingsubpixels in the binary map 1720 and the ground truth binary map 1404,as shown in FIG. 48.

The custom-weighted loss function gives more weight to the COMsubpixels, such that the cross-entropy loss is multiplied by acorresponding reward (or penalty) weight specified in a reward (orpenalty) matrix whenever a COM subpixel is misclassified. Additionaldetails about the custom-weighted loss function can be found in theAppendix entitled “Custom-Weighted Loss Function”.

The training 4800 includes hundreds, thousands, and/or millions ofiterations of forward propagation 4808 and backward propagation 4810,including parallelization techniques such as batching. The training data1504 includes, as the input image data 1702, a series of upsampled anddown-sized image sets. The training data 1504 is annotated with groundtruth labels by the annotator 2806. The training 2800 is operationalizedby the trainer 1510 using a stochastic gradient update algorithm such asADAM.

FIG. 49 illustrates another implementation of the input image data 1702fed to the binary classification model 4600 and the corresponding classlabels 4904 used to train the binary classification model 4600.

In the illustrated implementation, the input image data 1702 comprises aseries of upsampled and down-sized image sets 4902. The class labels4904 comprise two classes: (1) “no cluster center” and (2) “clustercenter”, which are distinguished using different output values. That is,(1) the light green units/subpixels 4906 represent subpixels that arepredicted by the binary classification model 4600 to not contain thecluster centers and (2) the dark green subpixels 4908 representunits/subpixels that are predicted by the binary classification model4600 to contain the cluster centers.

Inference

FIG. 50 is one implementation of template generation by the binaryclassification model 4600 during inference 5000 in which the binary map1720 is produced by the binary classification model 4600 as theinference output during the inference 5000. One example of the binarymap 1720 includes unit-wise binary classification scores 5010 thattogether represent the binary map 1720. In the softmax applications, thebinary map 1720 has a first array 5002 a of unit-wise classificationscores for the non-center class and a second array 5002 b of unit-wiseclassification scores for the cluster center class.

The inference 5000 includes hundreds, thousands, and/or millions ofiterations of forward propagation 5004, including parallelizationtechniques such as batching. The inference 5000 is performed oninference data 2908 that includes, as the input image data 1702, aseries of upsampled and down-sized image sets. The inference 5000 isoperationalized by the tester 2906.

In some implementations, the binary map 1720 is subjected topost-processing techniques discussed above, such as thresholding, peakdetection, and/or watershed segmentation to generate cluster metadata.

Peak Detection

FIG. 51 depicts one implementation of subjecting the binary map 1720 topeak detection to identify cluster centers. As discussed above, thebinary map 1720 is an array of units that classifies each subpixel basedon the predicted classification scores, with each unit in the arrayrepresenting a corresponding subpixel in the input. The classificationscores can be softmax scores or sigmoid scores.

In the softmax applications, the binary map 1720 includes two arrays:(1) a first array 5002 a of unit-wise classification scores for thenon-center class and (2) a second array 5002 b of unit-wiseclassification scores for the cluster center class. In both the arrays,each unit represents a corresponding subpixel in the input.

To determine which subpixels in the input contain the cluster centersand which do not contain the cluster centers, the peak locator 1806applies peak detection on the units in the binary map 1720. The peakdetection identifies those units that have classification scores (e.g.,softmax/sigmoid scores) above a preset threshold. The identified unitsare inferred as the cluster centers and their corresponding subpixels inthe input are determined to contain the cluster centers and stored ascluster center subpixels in a subpixel classifications data store 5102.Additional details about the peak locator 1806 can be found in theAppendix entitled “Peak Detection”.

The remaining units and their corresponding subpixels in the input aredetermined to not contain the cluster centers and stored as non-centersubpixels in the subpixel classifications data store 5102.

In some implementations, prior to applying the peak detection, thoseunits that have classification scores below a certain backgroundthreshold (e.g., 0.3) are set to zero. In some implementations, suchunits and their corresponding subpixels in the input are inferred todenote the background surrounding the clusters and stored as backgroundsubpixels in the subpixel classifications data store 5102. In otherimplementations, such units can be considered noise and ignored.

Example Model Outputs

FIG. 52a shows, on the left, an example binary map produced by thebinary classification model 4600. On the right, FIG. 52a also shows anexample ground truth binary map that the binary classification model4600 approximates during the training. The binary map has a plurality ofsubpixels and classifies each subpixel as either a cluster center or anon-center. Similarly, the ground truth binary map has a plurality ofsubpixels and classifies each subpixel as either a cluster center or anon-center.

Experimental Results and Observations

FIG. 52b illustrates performance of the binary classification model 4600using recall and precision statistics. Applying these statistics, thebinary classification model 4600 outperforms the RTA base caller.

Network Architecture

FIG. 53 is a table that shows an example architecture of the binaryclassification model 4600, along with details of the layers of thebinary classification model 4600, dimensionality of the output of thelayers, magnitude of the model parameters, and interconnections betweenthe layers. Similar details are disclosed in the Appendix titled “BinaryClassification Model Example Architecture”.

3. Ternary (Three Class) Classification Model

FIG. 54 illustrates one implementation of the ternary classificationmodel 5400. In the illustrated implementation, the ternaryclassification model 5400 is a deep fully convolutional segmentationneural network that processes the input image data 1702 through anencoder subnetwork and a corresponding decoder subnetwork. The encodersubnetwork includes a hierarchy of encoders. The decoder subnetworkincludes a hierarchy of decoders that map low resolution encoder featuremaps to a full input resolution ternary map 1718. In anotherimplementation, the ternary classification model 5400 is a U-Net networkwith skip connections between the decoder and the encoder. Additionaldetails about the segmentation networks can be found in the Appendixentitled “Segmentation Networks”.

Ternary Map

The final output layer of the ternary classification model 5400 is aunit-wise classification layer that produces a classification label foreach unit in an output array. In some implementations, the unit-wiseclassification layer is a subpixel-wise classification layer thatproduces a softmax classification score distribution for each subpixelin the ternary map 1718 across three classes, namely, a backgroundclass, a cluster center class, and a cluster/cluster interior class, andthe classification label for a given subpixel is determined from thecorresponding softmax classification score distribution.

The ternary map 1718 expresses each subpixel based on the predictedclassification scores. The ternary map 1718 also stores the predictedvalue classification scores in an array of units, with each unit in thearray representing a corresponding subpixel in the input.

Training

FIG. 55 is one implementation of training 5500 the ternaryclassification model 5400 using a backpropagation-based gradient updatetechnique that modifies parameters of the ternary classification model5400 until the ternary map 1718 of the ternary classification model 5400progressively approaches or matches training ground truth ternary maps1304.

In the illustrated implementation, the final output layer of the ternaryclassification model 5400 is a softmax-based subpixel-wiseclassification layer. In softmax implementations, the ground truthternary map generator 1402 assigns each ground truth subpixel either (i)a background value triplet (e.g., [1, 0, 0]), (ii) a cluster centervalue triplet (e.g., [0, 1, 0]), or (iii) a cluster/cluster interiorvalue triplet (e.g., [0, 0, 1]).

The background subpixels are assigned the background value triplet. Thecenter of mass (COM) subpixels are assigned the cluster center valuetriplet. The cluster/cluster interior subpixels are assigned thecluster/cluster interior value triplet.

In the background value triplet [1.0, 0], a first value [1] representsthe background class label, a second value [0] represents the clustercenter label, and a third value [0] represents the cluster/clusterinterior class label.

In the cluster center value triplet [0, 1, 0], a first value [0]represents the background class label, a second value [1] represents thecluster center label, and a third value [0] represents thecluster/cluster interior class label.

In the cluster/cluster interior value triplet [0, 0, 1], a first value[0] represents the background class label, a second value [0] representsthe cluster center label, and a third value [1] represents thecluster/cluster interior class label.

The ground truth ternary map 1304 expresses each subpixel based on theassigned value triplet. The ground truth ternary map 1304 also storesthe assigned triplets in an array of units, with each unit in the arrayrepresenting a corresponding subpixel in the input.

The training includes iteratively optimizing a loss function thatminimizes error 5506 (e.g., softmax error) between the ternary map 1718and the ground truth ternary map 1304, and updating parameters of theternary classification model 5400 based on the error 5506.

In one implementation, the loss function is a custom-weightedcategorical cross-entropy loss and the error 5506 is minimized on asubpixel-by-subpixel basis between predicted classification scores(e.g., softmax scores) and labelled class scores (e.g., softmax scores)of corresponding subpixels in the ternary map 1718 and the ground truthternary map 1304, as shown in FIG. 54.

The custom-weighted loss function gives more weight to the COMsubpixels, such that the cross-entropy loss is multiplied by acorresponding reward (or penalty) weight specified in a reward (orpenalty) matrix whenever a COM subpixel is misclassified. Additionaldetails about the custom-weighted loss function can be found in theAppendix entitled “Custom-Weighted Loss Function”.

The training 5500 includes hundreds, thousands, and/or millions ofiterations of forward propagation 5508 and backward propagation 5510,including parallelization techniques such as batching. The training data1504 includes, as the input image data 1702, a series of upsampled anddown-sized image sets. The training data 1504 is annotated with groundtruth labels by the annotator 2806. The training 5500 is operationalizedby the trainer 1510 using a stochastic gradient update algorithm such asADAM.

FIG. 56 illustrates one implementation of input image data 1702 fed tothe ternary classification model 5400 and the corresponding class labelsused to train the ternary classification model 5400.

In the illustrated implementation, the input image data 1702 comprises aseries of upsampled and down-sized image sets 5602. The class labels5604 comprise three classes: (1) “background class”, (2) “cluster centerclass”, and (3) “cluster interior class”, which are distinguished usingdifferent output values. For example, some of these different outputvalues can be visually represented as follows: (1) the greyunits/subpixels 5606 represent subpixels that are predicted by theternary classification model 5400 to be the background, (2) the darkgreen units/subpixels 5608 represent subpixels that are predicted by theternary classification model 5400 to contain the cluster centers, and(3) the light green subpixels 5610 represent subpixels that arepredicted by the ternary classification model 5400 to contain theinterior of the clusters.

Network Architecture

FIG. 57 is a table that shows an example architecture of the ternaryclassification model 5400, along with details of the layers of theternary classification model 5400, dimensionality of the output of thelayers, magnitude of the model parameters, and interconnections betweenthe layers. Similar details are disclosed in the Appendix titled“Ternary Classification Model Example Architecture”.

Inference

FIG. 58 is one implementation of template generation by the ternaryclassification model 5400 during inference 5800 in which the ternary map1718 is produced by the ternary classification model 5400 as theinference output during the inference 5800. One example of the ternarymap 1718 is disclosed in the Appendix titled“Ternary_Classification_Model_Sample_Ouput”. The Appendix includesunit-wise binary classification scores 5810 that together represent theternary map 1718. In the softmax applications, the Appendix has a firstarray 5802 a of unit-wise classification scores for the backgroundclass, a second array 5802 b of unit-wise classification scores for thecluster center class, and a third array 5802 c of unit-wiseclassification scores for the cluster/cluster interior class.

The inference 5800 includes hundreds, thousands, and/or millions ofiterations of forward propagation 5804, including parallelizationtechniques such as batching. The inference 5800 is performed oninference data 2908 that includes, as the input image data 1702, aseries of upsampled and down-sized image sets. The inference 5000 isoperationalized by the tester 2906.

In some implementations, the ternary map 1718 is produced by the ternaryclassification model 5400 using post-processing techniques discussedabove, such as thresholding, peak detection, and/or watershedsegmentation.

FIG. 59 graphically portrays the ternary map 1718 produced by theternary classification model 5400 in which each subpixel has a three-waysoftmax classification score distribution for the three correspondingclasses, namely, the background class 5906, the cluster center class5902, and the cluster/cluster interior class 5904.

FIG. 60 depicts an array of units produced by the ternary classificationmodel 5400, along with the unit-wise output values. As depicted, eachunit has three output values for the three corresponding classes,namely, the background class 5906, the cluster center class 5902, andthe cluster/cluster interior class 5904. For each classification(column-wise), each unit is assigned the class that has the highestoutput value, as indicated by the class in parenthesis under each unit.In some implementations, the output values 6002, 6004, and 6006 areanalyzed for each of the respective classes 5906, 5902, and 5904(row-wise).

Peak Detection & Watershed Segmentation

FIG. 61 shows one implementation of subjecting the ternary map 1718 topost-processing to identify cluster centers, cluster background, andcluster interior. As discussed above, the ternary map 1718 is an arrayof units that classifies each subpixel based on the predictedclassification scores, with each unit in the array representing acorresponding subpixel in the input. The classification scores can besoftmax scores.

In the softmax applications, the ternary map 1718 includes three arrays:(1) a first array 5802 a of unit-wise classification scores for thebackground class, (2) a second array 5802 b of unit-wise classificationscores for the cluster center class, and (3) a third array 5802 c ofunit-wise classification scores for the cluster interior class. In allthree arrays, each unit represents a corresponding subpixel in theinput.

To determine which subpixels in the input contain the cluster centers,which contain the interior of the clusters, and which contain thebackground, the peak locator 1806 applies peak detection on softmaxvalues in the ternary map 1718 for the cluster center class 5802 b. Thepeak detection identifies those units that have classification scores(e.g., softmax scores) above a preset threshold. The identified unitsare inferred as the cluster centers and their corresponding subpixels inthe input are determined to contain the cluster centers and stored ascluster center subpixels in a subpixel classifications and segmentationsdata store 6102. Additional details about the peak locator 1806 can befound in the Appendix entitled “Peak Detection”.

In some implementations, prior to applying the peak detection, thoseunits that have classification scores below a certain noise threshold(e.g., 0.3) are set to zero. Such units can be considered noise andignored.

Also, units that have classification scores for the background class5802 a above a certain background threshold (e.g., equal to or greaterthan 0.5) and their corresponding subpixels in the input are inferred todenote the background surrounding the clusters and stored as backgroundsubpixels in the subpixel classifications and segmentations data store6102.

Then, the watershed segmentation algorithm, operationalized by thewatershed segmenter 3102, is used to determine the shapes of theclusters. In some implementations, the background units/subpixels areused as a mask by the watershed segmentation algorithm. Classificationscores of the unit/subpixels inferred as the cluster centers and thecluster interior are summed to produce so-called “cluster labels”. Thecluster centers are used as watershed markers, for separation byintensity valleys by the watershed segmentation algorithm.

In one implementation, negativized cluster labels are provided as aninput image to the watershed segmenter 3102 that performs segmentationand produces the cluster shapes as disjointed regions of contiguouscluster interior subpixels separated by the background subpixels.Furthermore, each disjointed region includes a corresponding clustercenter subpixel. In some implementations, the corresponding clustercenter subpixel is the center of the disjointed region to which itbelongs. In other implementations, centers of mass (COM) of thedisjointed regions are calculated based on the underlying locationcoordinates and stored as new centers of the clusters.

The outputs of the watershed segmenter 3102 are stored in the subpixelclassifications and segmentations data store 6102. Additional detailsabout the watershed segmentation algorithm and other segmentationalgorithms can be found in Appendix entitled “Watershed Segmentation”.

Example outputs of the peak locator 1806 and the watershed segmenter3102 are shown in FIGS. 62a, 62b , 63, and 64.

Example Model Outputs

FIG. 62a shows example predictions of the ternary classification model5400. FIG. 62a shows four maps and each map has an array of units. Thefirst map 6202 (left most) shows each unit's output values for thecluster center class 5802 b. The second map 6204 shows each unit'soutput values for the cluster/cluster interior class 5802 c. The thirdmap 6206 (right most) shows each unit's output values for the backgroundclass 5802 a. The fourth map 6208 (bottom) is a binary mask of groundtruth ternary map 6008 that assigns each unit the class label that hasthe highest output value.

FIG. 62b illustrates other example predictions of the ternaryclassification model 5400. FIG. 62b shows four maps and each map has anarray of units. The first map 6212 (bottom left most) shows each unit'soutput values for the cluster/cluster interior class. The second map6214 shows each unit's output values for the cluster center class. Thethird map 6216 (bottom right most) shows each unit's output values forthe background class. The fourth map (top) 6210 is the ground truthternary map that assigns each unit the class label that has the highestoutput value.

FIG. 62c shows yet other example predictions of the ternaryclassification model 5400. FIG. 64 shows four maps and each map has anarray of units. The first map 6220 (bottom left most) shows each unit'soutput values for the cluster/cluster interior class. The second map6222 shows each unit's output values for the cluster center class. Thethird map 6224 (bottom right most) shows each unit's output values forthe background class. The fourth map 6218 (top) is the ground truthternary map that assigns each unit the class label that has the highestoutput value.

FIG. 63 depicts one implementation of deriving the cluster centers andcluster shapes from the output of the ternary classification model 5400in FIG. 62a by subjecting the output to post-processing. Thepost-processing (e.g., peak locating, watershed segmentation) generatescluster shape data and other metadata, which is identified in thecluster map 6310.

Experimental Results and Observations

FIG. 64 compares performance of the binary classification model 4600,the regression model 2600, and the RTA base caller. The performance isevaluated using a variety of sequencing metrics. One metric is the totalnumber of clusters detected (“# clusters”), which can be measured by thenumber of unique cluster centers that are detected. Another metric isthe number of detected clusters that pass the chastity filter (“% PF”(pass-filter)). During cycles 1-25 of a sequencing run, the chastityfilter removes the least reliable clusters from the image extractionresults. Clusters “pass filter” if no more than one base call has achastity value below 0.6 in the first 25 cycles. Chastity is defined asthe ratio of the brightest base intensity divided by the sum of thebrightest and the second brightest base intensities. This metric goesbeyond the quantity of the detected clusters and also conveys theirquality, i.e., how many of the detected clusters can be used foraccurate base calling and downstream secondary and ternary analysis suchas variant calling and variant pathogenicity annotation.

Other metrics that measure how good the detected clusters are fordownstream analysis include the number of aligned reads produced fromthe detected clusters (“% Aligned”), the number of duplicate readsproduced from the detected clusters (“% Duplicate”), the number of readsproduced from the detected clusters mismatching the reference sequencefor all reads aligned to the reference sequence (“% Mismatch”), thenumber of reads produced from the detected clusters whose portions donot match well to the reference sequence on either side and thus areignored for the alignment (“% soft clipped”), the number of bases calledfor the detected clusters with quality score 30 and above (“% Q30bases”), the number of paired reads produced from the detected clustersthat have both reads aligned inwards within a reasonable distance(“total proper read pairs”), and the number of unique or deduplicatedproper read pairs produced from the detected clusters (“non-duplicateproper read pairs”).

As shown in FIG. 64, both the binary classification model 4600 and theregression model 2600 outperform the RTA base caller at templategeneration on most of the metrics.

FIG. 65 compares the performance of the ternary classification model5400 with that of the RTA base caller under three contexts, fivesequencing metrics, and two run densities.

In the first context called “RTA”, the cluster centers are detected bythe RTA base caller, the intensity extraction from the clusters is doneby the RTA base caller, and the clusters are also base called using theRTA base caller. In the second context called “RTA IE”, the clustercenters are detected by the ternary classification model 5400; however,the intensity extraction from the clusters is done by the RTA basecaller and the clusters are also base called using the RTA base caller.In the third context called “Self IE”, the cluster centers are detectedby the ternary classification model 5400 and the intensity extractionfrom the clusters is done using the cluster shape-based intensityextraction techniques disclosed herein (note that the cluster shapeinformation is generated by the ternary classification model 5400); butthe clusters are base called using the RTA base caller.

The performance is compared between the ternary classification model5400 and the RTA base caller along five metrics: (1) the total number ofclusters detected (“# clusters”), (2) the number of detected clustersthat pass the chastity filter (“# PF”), (3) the number of unique ordeduplicated proper read pairs produced from the detected clusters (“#nondup proper read pairs”), (4) the rate of mismatches between asequence read produced from the detected clusters and a referencesequence after alignment (“% Mismatch rate”), and (5) bases called forthe detected clusters with quality score 30 and above (“% Q30”).

The performance is compared between the ternary classification model5400 and the RTA base caller under the three contexts and the fivemetrics for two types of sequencing runs: (1) a normal run with 20 pMlibrary concentration and (2) a dense run with 30 pM libraryconcentration.

As shown in FIG. 65, the ternary classification model 5400 outperformsthe RTA base caller on all the metrics.

Under the same three contexts, five metrics, and two run densities, FIG.66 shows that the regression model 2600 outperforms the RTA base calleron all the metrics.

FIG. 67 focuses on the penultimate layer 6702 of the neuralnetwork-based template generator 1512.

FIG. 68 visualizes what the penultimate layer 6702 of the neuralnetwork-based template generator 1512 has learned as a result of thebackpropagation-based gradient update training. The illustratedimplementation visualizes twenty-four out of the thirty-two convolutionfilters of the penultimate layer 6702 overlaid on the ground truthcluster shapes. As shown in FIG. 68, the penultimate layer 6702 haslearned the cluster metadata, including spatial distribution of theclusters such as cluster centers, cluster shapes, cluster sizes, clusterbackground, and cluster boundaries.

FIG. 69 overlays cluster center predictions of the binary classificationmodel 4600 (in blue) onto those of the RTA base caller (in pink). Thepredictions are made on sequencing image data from the Illumina NextSeqsequencer.

FIG. 70 overlays cluster center predictions made by the RTA base caller(in pink) onto visualization of the trained convolution filters of thepenultimate layer of the binary classification model 4600. Theseconvolution filters are learned as a result of training on sequencingimage data from the Illumina NextSeq sequencer.

FIG. 71 illustrates one implementation of training data used to trainthe neural network-based template generator 1512. In thisimplementation, the training data is obtained from dense flow cells thatproduce data with storm probe images. In another implementation, thetraining data is obtained from dense flow cells that produce data withfewer bridge amplification cycles.

FIG. 72 is one implementation of using beads for image registrationbased on cluster center predictions of the neural network-based templategenerator 1512.

FIG. 73 illustrates one implementation of cluster statistics of clustersidentified by the neural network-based template generator 1512. Thecluster statistics include cluster size based on number of contributivesubpixels and GC-content.

FIG. 74 shows how the neural network-based template generator 1512'sability to distinguish between adjacent clusters improves when thenumber of initial sequencing cycles for which the input image data 1702is used increases from five to seven. For five sequencing cycles, asingle cluster is identified by a single disjointed region of contiguoussubpixels. For seven sequencing cycles, the single cluster is segmentedinto two adjacent clusters, each having their own disjointed regions ofcontiguous subpixels.

FIG. 75 illustrates the difference in base calling performance when aRTA base caller uses ground truth center of mass (COM) location as thecluster center, as opposed to when a non-COM location is used as thecluster center.

FIG. 76 portrays the performance of the neural network-based templategenerator 1512 on extra detected clusters.

FIG. 77 shows different datasets used for training the neuralnetwork-based template generator 1512.

Sequencing System

FIGS. 78A and 78B depict one implementation of a sequencing system7800A. The sequencing system 7800A comprises a configurable processor7846. The configurable processor 7846 implements the base callingtechniques disclosed herein. The sequencing system is also referred toas a “sequencer.”

The sequencing system 7800A can operate to obtain any information ordata that relates to at least one of a biological or chemical substance.In some implementations, the sequencing system 7800A is a workstationthat may be similar to a bench-top device or desktop computer. Forexample, a majority (or all) of the systems and components forconducting the desired reactions can be within a common housing 7802.

In particular implementations, the sequencing system 7800A is a nucleicacid sequencing system configured for various applications, includingbut not limited to de novo sequencing, resequencing of whole genomes ortarget genomic regions, and metagenomics. The sequencer may also be usedfor DNA or RNA analysis. In some implementations, the sequencing system7800A may also be configured to generate reaction sites in a biosensor.For example, the sequencing system 7800A may be configured to receive asample and generate surface attached clusters of clonally amplifiednucleic acids derived from the sample. Each cluster may constitute or bepart of a reaction site in the biosensor.

The exemplary sequencing system 7800A may include a system receptacle orinterface 7810 that is configured to interact with a biosensor 7812 toperform desired reactions within the biosensor 7812. In the followingdescription with respect to FIG. 78A, the biosensor 7812 is loaded intothe system receptacle 7810. However, it is understood that a cartridgethat includes the biosensor 7812 may be inserted into the systemreceptacle 7810 and in some states the cartridge can be removedtemporarily or permanently. As described above, the cartridge mayinclude, among other things, fluidic control and fluidic storagecomponents.

In particular implementations, the sequencing system 7800A is configuredto perform a large number of parallel reactions within the biosensor7812. The biosensor 7812 includes one or more reaction sites wheredesired reactions can occur. The reaction sites may be, for example,immobilized to a solid surface of the biosensor or immobilized to beads(or other movable substrates) that are located within correspondingreaction chambers of the biosensor. The reaction sites can include, forexample, clusters of clonally amplified nucleic acids. The biosensor7812 may include a solid-state imaging device (e.g., CCD or CMOS imager)and a flow cell mounted thereto. The flow cell may include one or moreflow channels that receive a solution from the sequencing system 7800Aand direct the solution toward the reaction sites. Optionally, thebiosensor 7812 can be configured to engage a thermal element fortransferring thermal energy into or out of the flow channel.

The sequencing system 7800A may include various components, assemblies,and systems (or sub-systems) that interact with each other to perform apredetermined method or assay protocol for biological or chemicalanalysis. For example, the sequencing system 7800A includes a systemcontroller 7806 that may communicate with the various components,assemblies, and sub-systems of the sequencing system 7800A and also thebiosensor 7812. For example, in addition to the system receptacle 7810,the sequencing system 7800A may also include a fluidic control system7808 to control the flow of fluid throughout a fluid network of thesequencing system 7800A and the biosensor 7812; a fluid storage system7814 that is configured to hold all fluids (e.g., gas or liquids) thatmay be used by the bioassay system; a temperature control system 7804that may regulate the temperature of the fluid in the fluid network, thefluid storage system 7814, and/or the biosensor 7812; and anillumination system 7816 that is configured to illuminate the biosensor7812. As described above, if a cartridge having the biosensor 7812 isloaded into the system receptacle 7810, the cartridge may also includefluidic control and fluidic storage components.

Also shown, the sequencing system 7800A may include a user interface7818 that interacts with the user. For example, the user interface 7818may include a display 7820 to display or request information from a userand a user input device 7822 to receive user inputs. In someimplementations, the display 7820 and the user input device 7822 are thesame device. For example, the user interface 7818 may include atouch-sensitive display configured to detect the presence of anindividual's touch and also identify a location of the touch on thedisplay. However, other user input devices 7822 may be used, such as amouse, touchpad, keyboard, keypad, handheld scanner, voice-recognitionsystem, motion-recognition system, and the like. As will be discussed ingreater detail below, the sequencing system 7800A may communicate withvarious components, including the biosensor 7812 (e.g., in the form of acartridge), to perform the desired reactions. The sequencing system7800A may also be configured to analyze data obtained from the biosensorto provide a user with desired information.

The system controller 7806 may include any processor-based ormicroprocessor-based system, including systems using microcontrollers,reduced instruction set computers (RISC), application specificintegrated circuits (ASICs), field programmable gate array (FPGAs),coarse-grained reconfigurable architectures (CGRAs), logic circuits, andany other circuit or processor capable of executing functions describedherein. The above examples are exemplary only, and are thus not intendedto limit in any way the definition and/or meaning of the term systemcontroller. In the exemplary implementation, the system controller 7806executes a set of instructions that are stored in one or more storageelements, memories, or modules in order to at least one of obtain andanalyze detection data. Detection data can include a plurality ofsequences of pixel signals, such that a sequence of pixel signals fromeach of the millions of sensors (or pixels) can be detected over manybase calling cycles. Storage elements may be in the form of informationsources or physical memory elements within the sequencing system 7800A.

The set of instructions may include various commands that instruct thesequencing system 7800A or biosensor 7812 to perform specific operationssuch as the methods and processes of the various implementationsdescribed herein. The set of instructions may be in the form of asoftware program, which may form part of a tangible, non-transitorycomputer readable medium or media. As used herein, the terms “software”and “firmware” are interchangeable, and include any computer programstored in memory for execution by a computer, including RAM memory, ROMmemory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM)memory. The above memory types are exemplary only, and are thus notlimiting as to the types of memory usable for storage of a computerprogram.

The software may be in various forms such as system software orapplication software. Further, the software may be in the form of acollection of separate programs, or a program module within a largerprogram or a portion of a program module. The software also may includemodular programming in the form of object-oriented programming. Afterobtaining the detection data, the detection data may be automaticallyprocessed by the sequencing system 7800A, processed in response to userinputs, or processed in response to a request made by another processingmachine (e.g., a remote request through a communication link). In theillustrated implementation, the system controller 7806 includes ananalysis module 7844. In other implementations, system controller 7806does not include the analysis module 7844 and instead has access to theanalysis module 7844 (e.g., the analysis module 7844 may be separatelyhosted on cloud).

The system controller 7806 may be connected to the biosensor 7812 andthe other components of the sequencing system 7800A via communicationlinks. The system controller 7806 may also be communicatively connectedto off-site systems or servers. The communication links may behardwired, corded, or wireless. The system controller 7806 may receiveuser inputs or commands, from the user interface 7818 and the user inputdevice 7822.

The fluidic control system 7808 includes a fluid network and isconfigured to direct and regulate the flow of one or more fluids throughthe fluid network. The fluid network may be in fluid communication withthe biosensor 7812 and the fluid storage system 7814. For example,select fluids may be drawn from the fluid storage system 7814 anddirected to the biosensor 7812 in a controlled manner, or the fluids maybe drawn from the biosensor 7812 and directed toward, for example, awaste reservoir in the fluid storage system 7814. Although not shown,the fluidic control system 7808 may include flow sensors that detect aflow rate or pressure of the fluids within the fluid network. Thesensors may communicate with the system controller 7806.

The temperature control system 7804 is configured to regulate thetemperature of fluids at different regions of the fluid network, thefluid storage system 7814, and/or the biosensor 7812. For example, thetemperature control system 7804 may include a thermocycler thatinterfaces with the biosensor 7812 and controls the temperature of thefluid that flows along the reaction sites in the biosensor 7812. Thetemperature control system 7804 may also regulate the temperature ofsolid elements or components of the sequencing system 7800A or thebiosensor 7812. Although not shown, the temperature control system 7804may include sensors to detect the temperature of the fluid or othercomponents. The sensors may communicate with the system controller 7806.

The fluid storage system 7814 is in fluid communication with thebiosensor 7812 and may store various reaction components or reactantsthat are used to conduct the desired reactions therein. The fluidstorage system 7814 may also store fluids for washing or cleaning thefluid network and biosensor 7812 and for diluting the reactants. Forexample, the fluid storage system 7814 may include various reservoirs tostore samples, reagents, enzymes, other biomolecules, buffer solutions,aqueous, and non-polar solutions, and the like. Furthermore, the fluidstorage system 7814 may also include waste reservoirs for receivingwaste products from the biosensor 7812. In implementations that includea cartridge, the cartridge may include one or more of a fluid storagesystem, fluidic control system or temperature control system.Accordingly, one or more of the components set forth herein as relatingto those systems can be contained within a cartridge housing. Forexample, a cartridge can have various reservoirs to store samples,reagents, enzymes, other biomolecules, buffer solutions, aqueous, andnon-polar solutions, waste, and the like. As such, one or more of afluid storage system, fluidic control system or temperature controlsystem can be removably engaged with a bioassay system via a cartridgeor other biosensor.

The illumination system 7816 may include a light source (e.g., one ormore LEDs) and a plurality of optical components to illuminate thebiosensor. Examples of light sources may include lasers, arc lamps,LEDs, or laser diodes. The optical components may be, for example,reflectors, dichroics, beam splitters, collimators, lenses, filters,wedges, prisms, mirrors, detectors, and the like. In implementationsthat use an illumination system, the illumination system 7816 may beconfigured to direct an excitation light to reaction sites. As oneexample, fluorophores may be excited by green wavelengths of light, assuch the wavelength of the excitation light may be approximately 532 nm.In one implementation, the illumination system 7816 is configured toproduce illumination that is parallel to a surface normal of a surfaceof the biosensor 7812. In another implementation, the illuminationsystem 7816 is configured to produce illumination that is off-anglerelative to the surface normal of the surface of the biosensor 7812. Inyet another implementation, the illumination system 7816 is configuredto produce illumination that has plural angles, including some parallelillumination and some off-angle illumination.

The system receptacle or interface 7810 is configured to engage thebiosensor 7812 in at least one of a mechanical, electrical, and fluidicmanner. The system receptacle 7810 may hold the biosensor 7812 in adesired orientation to facilitate the flow of fluid through thebiosensor 7812. The system receptacle 7810 may also include electricalcontacts that are configured to engage the biosensor 7812 so that thesequencing system 7800A may communicate with the biosensor 7812 and/orprovide power to the biosensor 7812. Furthermore, the system receptacle7810 may include fluidic ports (e.g., nozzles) that are configured toengage the biosensor 7812. In some implementations, the biosensor 7812is removably coupled to the system receptacle 7810 in a mechanicalmanner, in an electrical manner, and also in a fluidic manner.

In addition, the sequencing system 7800A may communicate remotely withother systems or networks or with other bioassay systems 7800A.Detection data obtained by the bioassay system(s) 7800A may be stored ina remote database.

FIG. 78B is a block diagram of a system controller 7806 that can be usedin the system of FIG. 78A. In one implementation, the system controller7806 includes one or more processors or modules that can communicatewith one another. Each of the processors or modules may include analgorithm (e.g., instructions stored on a tangible and/or non-transitorycomputer readable storage medium) or sub-algorithms to performparticular processes. The system controller 7806 is illustratedconceptually as a collection of modules, but may be implementedutilizing any combination of dedicated hardware boards, DSPs,processors, etc. Alternatively, the system controller 7806 may beimplemented utilizing an off-the-shelf PC with a single processor ormultiple processors, with the functional operations distributed betweenthe processors. As a further option, the modules described below may beimplemented utilizing a hybrid configuration in which certain modularfunctions are performed utilizing dedicated hardware, while theremaining modular functions are performed utilizing an off-the-shelf PCand the like. The modules also may be implemented as software moduleswithin a processing unit.

During operation, a communication port 7850 may transmit information(e.g., commands) to or receive information (e.g., data) from thebiosensor 7812 (FIG. 78A) and/or the sub-systems 7808, 7814, 7804 (FIG.78A). In implementations, the communication port 7850 may output aplurality of sequences of pixel signals. A communication link 7834 mayreceive user input from the user interface 7818 (FIG. 78A) and transmitdata or information to the user interface 7818. Data from the biosensor7812 or sub-systems 7808, 7814, 7804 may be processed by the systemcontroller 7806 in real-time during a bioassay session. Additionally oralternatively, data may be stored temporarily in a system memory duringa bioassay session and processed in slower than real-time or off-lineoperation.

As shown in FIG. 78B, the system controller 7806 may include a pluralityof modules 7826-7848 that communicate with a main control module 7824,along with a central processing unit (CPU) 7852. The main control module7824 may communicate with the user interface 7818 (FIG. 78A). Althoughthe modules 7826-7848 are shown as communicating directly with the maincontrol module 7824, the modules 7826-7848 may also communicate directlywith each other, the user interface 7818, and the biosensor 7812. Also,the modules 7826-7848 may communicate with the main control module 7824through the other modules.

The plurality of modules 7826-7848 include system modules 7828-7832,7826 that communicate with the sub-systems 7808, 7814, 7804, and 7816,respectively. The fluidic control module 7828 may communicate with thefluidic control system 7808 to control the valves and flow sensors ofthe fluid network for controlling the flow of one or more fluids throughthe fluid network. The fluid storage module 7830 may notify the userwhen fluids are low or when the waste reservoir is at or near capacity.The fluid storage module 7830 may also communicate with the temperaturecontrol module 7832 so that the fluids may be stored at a desiredtemperature. The illumination module 7826 may communicate with theillumination system 7816 to illuminate the reaction sites at designatedtimes during a protocol, such as after the desired reactions (e.g.,binding events) have occurred. In some implementations, the illuminationmodule 7826 may communicate with the illumination system 7816 toilluminate the reaction sites at designated angles.

The plurality of modules 7826-7848 may also include a device module 7836that communicates with the biosensor 7812 and an identification module7838 that determines identification information relating to thebiosensor 7812. The device module 7836 may, for example, communicatewith the system receptacle 7810 to confirm that the biosensor hasestablished an electrical and fluidic connection with the sequencingsystem 7800A. The identification module 7838 may receive signals thatidentify the biosensor 7812. The identification module 7838 may use theidentity of the biosensor 7812 to provide other information to the user.For example, the identification module 7838 may determine and thendisplay a lot number, a date of manufacture, or a protocol that isrecommended to be run with the biosensor 7812.

The plurality of modules 7826-7848 also includes an analysis module 7844(also called signal processing module or signal processor) that receivesand analyzes the signal data (e.g., image data) from the biosensor 7812.Analysis module 7844 includes memory (e.g., RAM or Flash) to storedetection/image data. Detection data can include a plurality ofsequences of pixel signals, such that a sequence of pixel signals fromeach of the millions of sensors (or pixels) can be detected over manybase calling cycles. The signal data may be stored for subsequentanalysis or may be transmitted to the user interface 7818 to displaydesired information to the user. In some implementations, the signaldata may be processed by the solid-state imager (e.g., CMOS imagesensor) before the analysis module 7844 receives the signal data.

The analysis module 7844 is configured to obtain image data from thelight detectors at each of a plurality of sequencing cycles. The imagedata is derived from the emission signals detected by the lightdetectors and process the image data for each of the plurality ofsequencing cycles through the neural network-based template generator1512 and/or the neural network-based base caller 1514 and produce a basecall for at least some of the analytes at each of the plurality ofsequencing cycle. The light detectors can be part of one or moreover-head cameras (e.g., Illumina's GAIIx's CCD camera taking images ofthe clusters on the biosensor 7812 from the top), or can be part of thebiosensor 7812 itself (e.g., Illumina's iSeq's CMOS image sensorsunderlying the clusters on the biosensor 7812 and taking images of theclusters from the bottom).

The output of the light detectors is the sequencing images, eachdepicting intensity emissions of the clusters and their surroundingbackground. The sequencing images depict intensity emissions generatedas a result of nucleotide incorporation in the sequences during thesequencing. The intensity emissions are from associated analytes andtheir surrounding background. The sequencing images are stored in memory7848.

Protocol modules 7840 and 7842 communicate with the main control module7824 to control the operation of the sub-systems 7808, 7814, and 7804when conducting predetermined assay protocols. The protocol modules 7840and 7842 may include sets of instructions for instructing the sequencingsystem 7800A to perform specific operations pursuant to predeterminedprotocols. As shown, the protocol module may be asequencing-by-synthesis (SBS) module 7840 that is configured to issuevarious commands for performing sequencing-by-synthesis processes. InSBS, extension of a nucleic acid primer along a nucleic acid template ismonitored to determine the sequence of nucleotides in the template. Theunderlying chemical process can be polymerization (e.g., as catalyzed bya polymerase enzyme) or ligation (e.g., catalyzed by a ligase enzyme).In a particular polymerase-based SBS implementation, fluorescentlylabeled nucleotides are added to a primer (thereby extending the primer)in a template dependent fashion such that detection of the order andtype of nucleotides added to the primer can be used to determine thesequence of the template. For example, to initiate a first SBS cycle,commands can be given to deliver one or more labeled nucleotides, DNApolymerase, etc., into/through a flow cell that houses an array ofnucleic acid templates. The nucleic acid templates may be located atcorresponding reaction sites. Those reaction sites where primerextension causes a labeled nucleotide to be incorporated can be detectedthrough an imaging event. During an imaging event, the illuminationsystem 7816 may provide an excitation light to the reaction sites.Optionally, the nucleotides can further include a reversible terminationproperty that terminates further primer extension once a nucleotide hasbeen added to a primer. For example, a nucleotide analog having areversible terminator moiety can be added to a primer such thatsubsequent extension cannot occur until a deblocking agent is deliveredto remove the moiety. Thus, for implementations that use reversibletermination a command can be given to deliver a deblocking reagent tothe flow cell (before or after detection occurs). One or more commandscan be given to effect wash(es) between the various delivery steps. Thecycle can then be repeated n times to extend the primer by nnucleotides, thereby detecting a sequence of length n. Exemplarysequencing techniques are described, for example, in Bentley et al.,Nature 456:53-59 (20078); WO 04/0178497; U.S. Pat. No. 7,057,026; WO91/066778; WO 07/123744; U.S. Pat. Nos. 7,329,492; 7,211,414; 7,315,019;7,405,2781, and US 20078/01470780782, each of which is incorporatedherein by reference.

For the nucleotide delivery step of an SBS cycle, either a single typeof nucleotide can be delivered at a time, or multiple differentnucleotide types (e.g., A, C, T and G together) can be delivered. For anucleotide delivery configuration where only a single type of nucleotideis present at a time, the different nucleotides need not have distinctlabels since they can be distinguished based on temporal separationinherent in the individualized delivery. Accordingly, a sequencingmethod or apparatus can use single color detection. For example, anexcitation source need only provide excitation at a single wavelength orin a single range of wavelengths. For a nucleotide deliveryconfiguration where delivery results in multiple different nucleotidesbeing present in the flow cell at one time, sites that incorporatedifferent nucleotide types can be distinguished based on differentfluorescent labels that are attached to respective nucleotide types inthe mixture. For example, four different nucleotides can be used, eachhaving one of four different fluorophores. In one implementation, thefour different fluorophores can be distinguished using excitation infour different regions of the spectrum. For example, four differentexcitation radiation sources can be used. Alternatively, fewer than fourdifferent excitation sources can be used, but optical filtration of theexcitation radiation from a single source can be used to producedifferent ranges of excitation radiation at the flow cell.

In some implementations, fewer than four different colors can bedetected in a mixture having four different nucleotides. For example,pairs of nucleotides can be detected at the same wavelength, butdistinguished based on a difference in intensity for one member of thepair compared to the other, or based on a change to one member of thepair (e.g., via chemical modification, photochemical modification orphysical modification) that causes apparent signal to appear ordisappear compared to the signal detected for the other member of thepair. Exemplary apparatus and methods for distinguishing four differentnucleotides using detection of fewer than four colors are described forexample in US Pat. App. Ser. Nos. 61/5,378,294 and 61/61,978,778, whichare incorporated herein by reference in their entireties. U.S.application Ser. No. 13/624,200, which was filed on Sep. 21, 2012, isalso incorporated by reference in its entirety.

The plurality of protocol modules may also include a sample-preparation(or generation) module 7842 that is configured to issue commands to thefluidic control system 7808 and the temperature control system 7804 foramplifying a product within the biosensor 7812. For example, thebiosensor 7812 may be engaged to the sequencing system 7800A. Theamplification module 7842 may issue instructions to the fluidic controlsystem 7808 to deliver necessary amplification components to reactionchambers within the biosensor 7812. In other implementations, thereaction sites may already contain some components for amplification,such as the template DNA and/or primers. After delivering theamplification components to the reaction chambers, the amplificationmodule 7842 may instruct the temperature control system 7804 to cyclethrough different temperature stages according to known amplificationprotocols. In some implementations, the amplification and/or nucleotideincorporation is performed isothermally.

The SBS module 7840 may issue commands to perform bridge PCR whereclusters of clonal amplicons are formed on localized areas within achannel of a flow cell. After generating the amplicons through bridgePCR, the amplicons may be “linearized” to make single stranded templateDNA, or sstDNA, and a sequencing primer may be hybridized to a universalsequence that flanks a region of interest. For example, a reversibleterminator-based sequencing by synthesis method can be used as set forthabove or as follows.

Each base calling or sequencing cycle can extend an sstDNA by a singlebase which can be accomplished for example by using a modified DNApolymerase and a mixture of four types of nucleotides. The differenttypes of nucleotides can have unique fluorescent labels, and eachnucleotide can further have a reversible terminator that allows only asingle-base incorporation to occur in each cycle. After a single base isadded to the sstDNA, excitation light may be incident upon the reactionsites and fluorescent emissions may be detected. After detection, thefluorescent label and the terminator may be chemically cleaved from thesstDNA. Another similar base calling or sequencing cycle may follow. Insuch a sequencing protocol, the SBS module 7840 may instruct the fluidiccontrol system 7808 to direct a flow of reagent and enzyme solutionsthrough the biosensor 7812. Exemplary reversible terminator-based SBSmethods which can be utilized with the apparatus and methods set forthherein are described in US Patent Application Publication No.2007/0166705 A1, US Patent Application Publication No. 2006/017878901A1, U.S. Pat. No. 7,057,026, US Patent Application Publication No.2006/0240439 A1, US Patent Application Publication No. 2006/027814714709A1, PCT Publication No. WO 05/0657814, US Patent Application PublicationNo. 2005/014700900 A1, PCT Publication No. WO 06/078B199 and PCTPublication No. WO 07/01470251, each of which is incorporated herein byreference in its entirety. Exemplary reagents for reversibleterminator-based SBS are described in U.S. Pat. Nos. 7,541,444;7,057,026; 7,414,14716; U.S. Pat. Nos. 7,427,673; 7,566,537; 7,592,435and WO 07/1478353678, each of which is incorporated herein by referencein its entirety.

In some implementations, the amplification and SBS modules may operatein a single assay protocol where, for example, template nucleic acid isamplified and subsequently sequenced within the same cartridge.

The sequencing system 7800A may also allow the user to reconfigure anassay protocol. For example, the sequencing system 7800A may offeroptions to the user through the user interface 7818 for modifying thedetermined protocol. For example, if it is determined that the biosensor7812 is to be used for amplification, the sequencing system 7800A mayrequest a temperature for the annealing cycle. Furthermore, thesequencing system 7800A may issue warnings to a user if a user hasprovided user inputs that are generally not acceptable for the selectedassay protocol.

In implementations, the biosensor 7812 includes millions of sensors (orpixels), each of which generates a plurality of sequences of pixelsignals over successive base calling cycles. The analysis module 7844detects the plurality of sequences of pixel signals and attributes themto corresponding sensors (or pixels) in accordance to the row-wiseand/or column-wise location of the sensors on an array of sensors.

FIG. 79 is a simplified block diagram of a system for analysis of sensordata from the sequencing system 7800A, such as base call sensor outputs.In the example of FIG. 79, the system includes the configurableprocessor 7846. The configurable processor 7846 can execute a basecaller (e.g., the neural network-based template generator 1512 and/orthe neural network-based base caller 1514) in coordination with aruntime program executed by the central processing unit (CPU) 7852(i.e., a host processor). The sequencing system 7800A comprises thebiosensor 7812 and flow cells. The flow cells can comprise one or moretiles in which clusters of genetic material are exposed to a sequence ofanalyte flows used to cause reactions in the clusters to identify thebases in the genetic material. The sensors sense the reactions for eachcycle of the sequence in each tile of the flow cell to provide tiledata. Genetic sequencing is a data intensive operation, which translatesbase call sensor data into sequences of base calls for each cluster ofgenetic material sensed in during a base call operation.

The system in this example includes the CPU 7852, which executes aruntime program to coordinate the base call operations, memory 7848B tostore sequences of arrays of tile data, base call reads produced by thebase calling operation, and other information used in the base calloperations. Also, in this illustration the system includes memory 7848Ato store a configuration file (or files), such as FPGA bit files, andmodel parameters for the neural networks used to configure andreconfigure the configurable processor 7846, and execute the neuralnetworks. The sequencing system 7800A can include a program forconfiguring a configurable processor and in some embodiments areconfigurable processor to execute the neural networks.

The sequencing system 7800A is coupled by a bus 7902 to the configurableprocessor 7846. The bus 7902 can be implemented using a high throughputtechnology, such as in one example bus technology compatible with thePCIe standards (Peripheral Component Interconnect Express) currentlymaintained and developed by the PCI-SIG (PCI Special Interest Group).Also in this example, a memory 7848A is coupled to the configurableprocessor 7846 by bus 7906. The memory 7848A can be on-board memory,disposed on a circuit board with the configurable processor 7846. Thememory 7848A is used for high speed access by the configurable processor7846 of working data used in the base call operation. The bus 7906 canalso be implemented using a high throughput technology, such as bustechnology compatible with the PCIe standards.

Configurable processors, including field programmable gate arrays FPGAs,coarse grained reconfigurable arrays CGRAs, and other configurable andreconfigurable devices, can be configured to implement a variety offunctions more efficiently or faster than might be achieved using ageneral purpose processor executing a computer program. Configuration ofconfigurable processors involves compiling a functional description toproduce a configuration file, referred to sometimes as a bitstream orbit file, and distributing the configuration file to the configurableelements on the processor. The configuration file defines the logicfunctions to be executed by the configurable processor, by configuringthe circuit to set data flow patterns, use of distributed memory andother on-chip memory resources, lookup table contents, operations ofconfigurable logic blocks and configurable execution units likemultiply-and-accumulate units, configurable interconnects and otherelements of the configurable array. A configurable processor isreconfigurable if the configuration file may be changed in the field, bychanging the loaded configuration file. For example, the configurationfile may be stored in volatile SRAM elements, in non-volatile read-writememory elements, and in combinations of the same, distributed among thearray of configurable elements on the configurable or reconfigurableprocessor. A variety of commercially available configurable processorsare suitable for use in a base calling operation as described herein.Examples include Google's Tensor Processing Unit (TPU)™, rackmountsolutions like GX4 Rackmount Series™, GX9 Rackmount Series™, NVIDIADGX-1™, Microsoft' Stratix V FPGA™, Graphcore's Intelligent ProcessorUnit (IPU)™, Qualcomm's Zeroth Platform™ with Snapdragon Processors™,NVIDIA's Volta™, NVIDIA's DRIVE PX™, NVIDIA's JETSON TX1/TX2 MODULE™,Intel's Nirvana™, Movidius VPU™, Fujitsu DPI™, ARM's DynamrcIQ™, IBMTrueNorth™, Lambda GPU Server with TestaV100s™, Xilinx Alveo™ U200,Xilinx Alveo™ U250, Xilinx Alveo™ U280, Intel/Altera Stratix™ GX2800,Intel/Altera Stratix™ GX2800, and Intel Stratix™ GX10M. In someexamples, a host CPU can be implemented on the same integrated circuitas the configurable processor.

Embodiments described herein implement the neural network-based templategenerator 1512 and/or the neural network-based base caller 1514 usingthe configurable processor 7846. The configuration file for theconfigurable processor 7846 can be implemented by specifying the logicfunctions to be executed using a high level description language HDL ora register transfer level RTL language specification. The specificationcan be compiled using the resources designed for the selectedconfigurable processor to generate the configuration file. The same orsimilar specification can be compiled for the purposes of generating adesign for an application-specific integrated circuit which may not be aconfigurable processor.

Alternatives for the configurable processor configurable processor 7846,in all embodiments described herein, therefore include a configuredprocessor comprising an application specific ASIC or special purposeintegrated circuit or set of integrated circuits, or a system-on-a-chipSOC device, or a graphics processing unit (GPU) processor or acoarse-grained reconfigurable architecture (CGRA) processor, configuredto execute a neural network based base call operation as describedherein.

In general, configurable processors and configured processors describedherein, as configured to execute runs of a neural network, are referredto herein as neural network processors.

The configurable processor 7846 is configured in this example by aconfiguration file loaded using a program executed by the CPU 7852, orby other sources, which configures the array of configurable elements7916 (e.g., configuration logic blocks (CLB) such as look up tables(LUTs), flip-flops, compute processing units (PMUs), and compute memoryunits (CMUs), configurable I/O blocks, programmable interconnects), onthe configurable processor to execute the base call function. In thisexample, the configuration includes data flow logic 7908 which iscoupled to the buses 7902 and 7906 and executes functions fordistributing data and control parameters among the elements used in thebase call operation.

Also, the configurable processor 7846 is configured with base callexecution logic 7908 to execute the neural network-based templategenerator 1512 and/or the neural network-based base caller 1514. Thelogic 7908 comprises multi-cycle execution clusters (e.g., 7914) which,in this example, includes execution cluster 1 through execution clusterX. The number of multi-cycle execution clusters can be selectedaccording to a trade-off involving the desired throughput of theoperation, and the available resources on the configurable processor7846.

The multi-cycle execution clusters are coupled to the data flow logic7908 by data flow paths 7910 implemented using configurable interconnectand memory resources on the configurable processor 7846. Also, themulti-cycle execution clusters are coupled to the data flow logic 7908by control paths 7912 implemented using configurable interconnect andmemory resources for example on the configurable processor 7846, whichprovide control signals indicating available execution clusters,readiness to provide input units for execution of a run of the neuralnetwork-based template generator 1512 and/or the neural network-basedbase caller 1514 to the available execution clusters, readiness toprovide trained parameters for the neural network-based templategenerator 1512 and/or the neural network-based base caller 1514,readiness to provide output patches of base call classification data,and other control data used for execution of the neural network-basedtemplate generator 1512 and/or the neural network-based base caller1514.

The configurable processor 7846 is configured to execute runs of theneural network-based template generator 1512 and/or the neuralnetwork-based base caller 1514 using trained parameters to produceclassification data for the sensing cycles of the base callingoperation. A run of the neural network-based template generator 1512and/or the neural network-based base caller 1514 is executed to produceclassification data for a subject sensing cycle of the base callingoperation. A run of the neural network-based template generator 1512and/or the neural network-based base caller 1514 operates on a sequenceincluding a number N of arrays of tile data from respective sensingcycles of N sensing cycles, where the N sensing cycles provide sensordata for different base call operations for one base position peroperation in time sequence in the examples described herein. Optionally,some of the N sensing cycles can be out of sequence if needed accordingto a particular neural network model being executed. The number N can beany number greater than one. In some examples described herein, sensingcycles of the N sensing cycles represent a set of sensing cycles for atleast one sensing cycle preceding the subject sensing cycle and at leastone sensing cycle following the subject cycle in time sequence. Examplesare described herein in which the number N is an integer equal to orgreater than five.

The data flow logic 7908 is configured to move tile data and at leastsome trained parameters of the model parameters from the memory 7848A tothe configurable processor 7846 for runs of the neural network-basedtemplate generator 1512 and/or the neural network-based base caller1514, using input units for a given run including tile data forspatially aligned patches of the N arrays. The input units can be movedby direct memory access operations in one DMA operation, or in smallerunits moved during available time slots in coordination with theexecution of the neural network deployed.

Tile data for a sensing cycle as described herein can comprise an arrayof sensor data having one or more features. For example, the sensor datacan comprise two images which are analyzed to identify one of four basesat a base position in a genetic sequence of DNA, RNA, or other geneticmaterial. The tile data can also include metadata about the images andthe sensors. For example, in embodiments of the base calling operation,the tile data can comprise information about alignment of the imageswith the clusters such as distance from center information indicatingthe distance of each pixel in the array of sensor data from the centerof a cluster of genetic material on the tile.

During execution of the neural network-based template generator 1512and/or the neural network-based base caller 1514 as described below,tile data can also include data produced during execution of the neuralnetwork-based template generator 1512 and/or the neural network-basedbase caller 1514, referred to as intermediate data, which can be reusedrather than recomputed during a run of the neural network-based templategenerator 1512 and/or the neural network-based base caller 1514. Forexample, during execution of the neural network-based template generator1512 and/or the neural network-based base caller 1514, the data flowlogic 7908 can write intermediate data to the memory 7848A in place ofthe sensor data for a given patch of an array of tile data. Embodimentslike this are described in more detail below.

As illustrated, a system is described for analysis of base call sensoroutput, comprising memory (e.g., 7848A) accessible by the runtimeprogram storing tile data including sensor data for a tile from sensingcycles of a base calling operation. Also, the system includes a neuralnetwork processor, such as configurable processor 7846 having access tothe memory. The neural network processor is configured to execute runsof a neural network using trained parameters to produce classificationdata for sensing cycles. As described herein, a run of the neuralnetwork is operating on a sequence of N arrays of tile data fromrespective sensing cycles of N sensing cycles, including a subjectcycle, to produce the classification data for the subject cycle. Thedata flow logic 908 is provided to move tile data and the trainedparameters from the memory to the neural network processor for runs ofthe neural network using input units including data for spatiallyaligned patches of the N arrays from respective sensing cycles of Nsensing cycles.

Also, a system is described in which the neural network processor hasaccess to the memory, and includes a plurality of execution clusters,the execution clusters in the plurality of execution clusters configuredto execute a neural network. The data flow logic 7908 has access to thememory and to execution clusters in the plurality of execution clusters,to provide input units of tile data to available execution clusters inthe plurality of execution clusters, the input units including a numberN of spatially aligned patches of arrays of tile data from respectivesensing cycles, including a subject sensing cycle, and to cause theexecution clusters to apply the N spatially aligned patches to theneural network to produce output patches of classification data for thespatially aligned patch of the subject sensing cycle, where N is greaterthan 1.

FIG. 80 is a simplified diagram showing aspects of the base callingoperation, including functions of a runtime program executed by a hostprocessor. In this diagram, the output of image sensors from a flow cellare provided on lines 8000 to image processing threads 8001, which canperform processes on images such as alignment and arrangement in anarray of sensor data for the individual tiles and resampling of images,and can be used by processes which calculate a tile cluster mask foreach tile in the flow cell, which identifies pixels in the array ofsensor data that correspond to clusters of genetic material on thecorresponding tile of the flow cell. The outputs of the image processingthreads 8001 are provided on lines 8002 to a dispatch logic 8010 in theCPU which routes the arrays of tile data to a data cache 8004 (e.g., SSDstorage) on a high-speed bus 8003, or on high-speed bus 8005 to theneural network processor hardware 8020, such as the configurableprocessor 7846 of FIG. 79, according to the state of the base callingoperation. The processed and transformed images can be stored on thedata cache 8004 for sensing cycles that were previously used. Thehardware 8020 returns classification data output by the neural networkto the dispatch logic 8080, which passes the information to the datacache 8004, or on lines 8011 to threads 8002 that perform base call andquality score computations using the classification data, and canarrange the data in standard formats for base call reads. The outputs ofthe threads 8002 that perform base calling and quality scorecomputations are provided on lines 8012 to threads 8003 that aggregatethe base call reads, perform other operations such as data compression,and write the resulting base call outputs to specified destinations forutilization by the customers.

In some embodiments, the host can include threads (not shown) thatperform final processing of the output of the hardware 8020 in supportof the neural network. For example, the hardware 8020 can provideoutputs of classification data from a final layer of the multi-clusterneural network. The host processor can execute an output activationfunction, such as a softmax function, over the classification data toconfigure the data for use by the base call and quality score threads8002. Also, the host processor can execute input operations (not shown),such as batch normalization of the tile data prior to input to thehardware 8020.

FIG. 81 is a simplified diagram of a configuration of a configurableprocessor 7846 such as that of FIG. 79. In FIG. 81, the configurableprocessor 7846 comprises an FPGA with a plurality of high speed PCIeinterfaces. The FPGA is configured with a wrapper 8100 which comprisesthe data flow logic 7908 described with reference to FIG. 79. Thewrapper 8100 manages the interface and coordination with a runtimeprogram in the CPU across the CPU communication link 8109 and managescommunication with the on-board DRAM 8102 (e.g., memory 7848A) via DRAMcommunication link 8110. The data flow logic 7908 in the wrapper 8100provides patch data retrieved by traversing the arrays of tile data onthe on-board DRAM 8102 for the number N cycles to a cluster 8101, andretrieves process data 8115 from the cluster 8101 for delivery back tothe on-board DRAM 8102. The wrapper 8100 also manages transfer of databetween the on-board DRAM 8102 and host memory, for both the inputarrays of tile data, and for the output patches of classification data.The wrapper transfers patch data on line 8113 to the allocated cluster8101. The wrapper provides trained parameters, such as weights andbiases on line 8112 to the cluster 8101 retrieved from the on-board DRAM8102. The wrapper provides configuration and control data on line 8111to the cluster 8101 provided from, or generated in response to, theruntime program on the host via the CPU communication link 8109. Thecluster can also provide status signals on line 8116 to the wrapper8100, which are used in cooperation with control signals from the hostto manage traversal of the arrays of tile data to provide spatiallyaligned patch data, and to execute the multi-cycle neural network overthe patch data using the resources of the cluster 8101.

As mentioned above, there can be multiple clusters on a singleconfigurable processor managed by the wrapper 8100 configured forexecuting on corresponding ones of multiple patches of the tile data.Each cluster can be configured to provide classification data for basecalls in a subject sensing cycle using the tile data of multiple sensingcycles described herein.

In examples of the system, model data, including kernel data like filterweights and biases can be sent from the host CPU to the configurableprocessor, so that the model can be updated as a function of cyclenumber. A base calling operation can comprise, for a representativeexample, on the order of hundreds of sensing cycles. Base callingoperation can include paired end reads in some embodiments. For example,the model trained parameters may be updated once every 20 cycles (orother number of cycles), or according to update patterns implemented forparticular systems and neural network models. In some embodimentsincluding paired end reads in which a sequence for a given string in agenetic cluster on a tile includes a first part extending from a firstend down (or up) the string, and a second part extending from a secondend up (or down) the string, the trained parameters can be updated onthe transition from the first part to the second part.

In some examples, image data for multiple cycles of sensing data for atile can be sent from the CPU to the wrapper 8100. The wrapper 8100 canoptionally do some pre-processing and transformation of the sensing dataand write the information to the on-board DRAM 8102. The input tile datafor each sensing cycle can include arrays of sensor data including onthe order of 4000×3000 pixels per sensing cycle per tile or more, withtwo features representing colors of two images of the tile, and one ortwo bytes per feature per pixel. For an embodiment in which the number Nis three sensing cycles to be used in each run of the multi-cycle neuralnetwork, the array of tile data for each run of the multi-cycle neuralnetwork can consume on the order of hundreds of megabytes per tile. Insome embodiments of the system, the tile data also includes an array ofDFC data, stored once per tile, or other type of metadata about thesensor data and the tiles.

In operation, when a multi-cycle cluster is available, the wrapperallocates a patch to the cluster. The wrapper fetches a next patch oftile data in the traversal of the tile and sends it to the allocatedcluster along with appropriate control and configuration information.The cluster can be configured with enough memory on the configurableprocessor to hold a patch of data including patches from multiple cyclesin some systems, that is being worked on in place, and a patch of datathat is to be worked on when the current patch of processing is finishedusing a ping-pong buffer technique or raster scanning technique invarious embodiments.

When an allocated cluster completes its run of the neural network forthe current patch and produces an output patch, it will signal thewrapper. The wrapper will read the output patch from the allocatedcluster, or alternatively the allocated cluster will push the data outto the wrapper. Then the wrapper will assemble output patches for theprocessed tile in the DRAM 8102. When the processing of the entire tilehas been completed, and the output patches of data transferred to theDRAM, the wrapper sends the processed output array for the tile back tothe host/CPU in a specified format. In some embodiments, the on-boardDRAM 8102 is managed by memory management logic in the wrapper 8100. Theruntime program can control the sequencing operations to completeanalysis of all the arrays of tile data for all the cycles in the run ina continuous flow to provide real time analysis.

Technical Improvements and Terminology

Base calling includes incorporation or attachment of afluorescently-labeled tag with an analyte. The analyte can be anucleotide or an oligonucleotide, and the tag can be for a particularnucleotide type (A, C, T, or G). Excitation light is directed toward theanalyte having the tag, and the tag emits a detectable fluorescentsignal or intensity emission. The intensity emission is indicative ofphotons emitted by the excited tag that is chemically attached to theanalyte.

Throughout this application, including the claims, when phrases such asor similar to “images, image data, or image regions depicting intensityemissions of analytes and their surrounding background” are used, theyrefer to the intensity emissions of the tags attached to the analytes. Aperson skilled in the art will appreciate that the intensity emissionsof the attached tags are representative of or equivalent to theintensity emissions of the analytes to which the tags are attached, andare therefore used interchangeably. Similarly, properties of theanalytes refer to properties of the tags attached to the analytes or ofthe intensity emissions from the attached tags. For example, a center ofan analyte refers to the center of the intensity emissions emitted by atag attached to the analyte. In another example, the surroundingbackground of an analyte refers to the surrounding background of theintensity emissions emitted by a tag attached to the analyte.

All literature and similar material cited in this application,including, but not limited to, patents, patent applications, articles,books, treatises, and web pages, regardless of the format of suchliterature and similar materials, are expressly incorporated byreference in their entirety. In the event that one or more of theincorporated literature and similar materials differs from orcontradicts this application, including but not limited to definedterms, term usage, described techniques, or the like, this applicationcontrols.

The technology disclosed uses neural networks to improve the quality andquantity of nucleic acid sequence information that can be obtained froma nucleic acid sample such as a nucleic acid template or its complement,for instance, a DNA or RNA polynucleotide or other nucleic acid sample.Accordingly, certain implementations of the technology disclosed providehigher throughput polynucleotide sequencing, for instance, higher ratesof collection of DNA or RNA sequence data, greater efficiency insequence data collection, and/or lower costs of obtaining such sequencedata, relative to previously available methodologies.

The technology disclosed uses neural networks to identify the center ofa solid-phase nucleic acid cluster and to analyze optical signals thatare generated during sequencing of such clusters, to discriminateunambiguously between adjacent, abutting or overlapping clusters inorder to assign a sequencing signal to a single, discrete sourcecluster. These and related implementations thus permit retrieval ofmeaningful information, such as sequence data, from regions ofhigh-density cluster arrays where useful information could notpreviously be obtained from such regions due to confounding effects ofoverlapping or very closely spaced adjacent clusters, including theeffects of overlapping signals (e.g., as used in nucleic acidsequencing) emanating therefrom.

As described in greater detail below, in certain implementations thereis provided a composition that comprises a solid support havingimmobilized thereto one or a plurality of nucleic acid clusters asprovided herein. Each cluster comprises a plurality of immobilizednucleic acids of the same sequence and has an identifiable center havinga detectable center label as provided herein, by which the identifiablecenter is distinguishable from immobilized nucleic acids in asurrounding region in the cluster. Also described herein are methods formaking and using such clusters that have identifiable centers.

The presently disclosed implementations will find uses in numeroussituations where advantages are obtained from the ability to identify,determine, annotate, record or otherwise assign the position of asubstantially central location within a cluster, such as high-throughputnucleic acid sequencing, development of image analysis algorithms forassigning optical or other signals to discrete source clusters, andother applications where recognition of the center of an immobilizednucleic acid cluster is desirable and beneficial.

In certain implementations, the present invention contemplates methodsthat relate to high-throughput nucleic acid analysis such as nucleicacid sequence determination (e.g., “sequencing”). Exemplaryhigh-throughput nucleic acid analyses include without limitation de novosequencing, re-sequencing, whole genome sequencing, gene expressionanalysis, gene expression monitoring, epigenetic analysis, genomemethylation analysis, allele specific primer extension (APSE), geneticdiversity profiling, whole genome polymorphism discovery and analysis,single nucleotide polymorphism analysis, hybridization based sequencedetermination methods, and the like. One skilled in the art willappreciate that a variety of different nucleic acids can be analyzedusing the methods and compositions of the present invention.

Although the implementations of the present invention are described inrelation to nucleic acid sequencing, they are applicable in any fieldwhere image data acquired at different time points, spatial locations orother temporal or physical perspectives is analyzed. For example, themethods and systems described herein are useful in the fields ofmolecular and cell biology where image data from microarrays, biologicalspecimens, cells, organisms and the like is acquired and at differenttime points or perspectives and analyzed. Images can be obtained usingany number of techniques known in the art including, but not limited to,fluorescence microscopy, light microscopy, confocal microscopy, opticalimaging, magnetic resonance imaging, tomography scanning or the like. Asanother example, the methods and systems described herein can be appliedwhere image data obtained by surveillance, aerial or satellite imagingtechnologies and the like is acquired at different time points orperspectives and analyzed. The methods and systems are particularlyuseful for analyzing images obtained for a field of view in which theanalytes being viewed remain in the same locations relative to eachother in the field of view. The analytes may however havecharacteristics that differ in separate images, for example, theanalytes may appear different in separate images of the field of view.For example, the analytes may appear different with regard to the colorof a given analyte detected in different images, a change in theintensity of signal detected for a given analyte in different images, oreven the appearance of a signal for a given analyte in one image anddisappearance of the signal for the analyte in another image.

Examples described herein may be used in various biological or chemicalprocesses and systems for academic or commercial analysis. Morespecifically, examples described herein may be used in various processesand systems where it is desired to detect an event, property, quality,or characteristic that is indicative of a designated reaction. Forexample, examples described herein include light detection devices,biosensors, and their components, as well as bioassay systems thatoperate with biosensors. In some examples, the devices, biosensors andsystems may include a flow cell and one or more light sensors that arecoupled together (removably or fixedly) in a substantially unitarystructure.

The devices, biosensors and bioassay systems may be configured toperform a plurality of designated reactions that may be detectedindividually or collectively. The devices, biosensors and bioassaysystems may be configured to perform numerous cycles in which theplurality of designated reactions occurs in parallel. For example, thedevices, biosensors and bioassay systems may be used to sequence a densearray of DNA features through iterative cycles of enzymatic manipulationand light or image detection/acquisition. As such, the devices,biosensors and bioassay systems (e.g., via one or more cartridges) mayinclude one or more microfluidic channel that delivers reagents or otherreaction components in a reaction solution to a reaction site of thedevices, biosensors and bioassay systems. In some examples, the reactionsolution may be substantially acidic, such as comprising a pH of lessthan or equal to about 5, or less than or equal to about 4, or less thanor equal to about 3. In some other examples, the reaction solution maybe substantially alkaline/basic, such as comprising a pH of greater thanor equal to about 8, or greater than or equal to about 9, or greaterthan or equal to about 10. As used herein, the term “acidity” andgrammatical variants thereof refer to a pH value of less than about 7,and the terms “basicity,” “alkalinity” and grammatical variants thereofrefer to a pH value of greater than about 7.

In some examples, the reaction sites are provided or spaced apart in apredetermined manner, such as in a uniform or repeating pattern. In someother examples, the reaction sites are randomly distributed. Each of thereaction sites may be associated with one or more light guides and oneor more light sensors that detect light from the associated reactionsite. In some examples, the reaction sites are located in reactionrecesses or chambers, which may at least partially compartmentalize thedesignated reactions therein.

As used herein, a “designated reaction” includes a change in at leastone of a chemical, electrical, physical, or optical property (orquality) of a chemical or biological substance of interest, such as ananalyte-of-interest. In particular examples, a designated reaction is apositive binding event, such as incorporation of a fluorescently labeledbiomolecule with an analyte-of-interest, for example. More generally, adesignated reaction may be a chemical transformation, chemical change,or chemical interaction. A designated reaction may also be a change inelectrical properties. In particular examples, a designated reactionincludes the incorporation of a fluorescently-labeled molecule with ananalyte. The analyte may be an oligonucleotide and thefluorescently-labeled molecule may be a nucleotide. A designatedreaction may be detected when an excitation light is directed toward theoligonucleotide having the labeled nucleotide, and the fluorophore emitsa detectable fluorescent signal. In alternative examples, the detectedfluorescence is a result of chemiluminescence or bioluminescence. Adesignated reaction may also increase fluorescence (or Förster)resonance energy transfer (FRET), for example, by bringing a donorfluorophore in proximity to an acceptor fluorophore, decrease FRET byseparating donor and acceptor fluorophores, increase fluorescence byseparating a quencher from a fluorophore, or decrease fluorescence byco-locating a quencher and fluorophore.

As used herein, a “reaction solution,” “reaction component” or“reactant” includes any substance that may be used to obtain at leastone designated reaction. For example, potential reaction componentsinclude reagents, enzymes, samples, other biomolecules, and buffersolutions, for example. The reaction components may be delivered to areaction site in a solution and/or immobilized at a reaction site. Thereaction components may interact directly or indirectly with anothersubstance, such as an analyte-of-interest immobilized at a reactionsite. As noted above, the reaction solution may be substantially acidic(i.e., include a relatively high acidity) (e.g., comprising a pH of lessthan or equal to about 5, a pH less than or equal to about 4, or a pHless than or equal to about 3) or substantially alkaline/basic (i.e.,include a relatively high alkalinity/basicity) (e.g., comprising a pH ofgreater than or equal to about 8, a pH of greater than or equal to about9, or a pH of greater than or equal to about 10).

As used herein, the term “reaction site” is a localized region where atleast one designated reaction may occur. A reaction site may includesupport surfaces of a reaction structure or substrate where a substancemay be immobilized thereon. For example, a reaction site may include asurface of a reaction structure (which may be positioned in a channel ofa flow cell) that has a reaction component thereon, such as a colony ofnucleic acids thereon. In some such examples, the nucleic acids in thecolony have the same sequence, being for example, clonal copies of asingle stranded or double stranded template. However, in some examples areaction site may contain only a single nucleic acid molecule, forexample, in a single stranded or double stranded form.

A plurality of reaction sites may be randomly distributed along thereaction structure or arranged in a predetermined manner (e.g.,side-by-side in a matrix, such as in microarrays). A reaction site canalso include a reaction chamber or recess that at least partiallydefines a spatial region or volume configured to compartmentalize thedesignated reaction. As used herein, the term “reaction chamber” or“reaction recess” includes a defined spatial region of the supportstructure (which is often in fluid communication with a flow channel). Areaction recess may be at least partially separated from the surroundingenvironment other or spatial regions. For example, a plurality ofreaction recesses may be separated from each other by shared walls, suchas a detection surface. As a more specific example, the reactionrecesses may be nanowells comprising an indent, pit, well, groove,cavity or depression defined by interior surfaces of a detection surfaceand have an opening or aperture (i.e., be open-sided) so that thenanowells can be in fluid communication with a flow channel.

In some examples, the reaction recesses of the reaction structure aresized and shaped relative to solids (including semi-solids) so that thesolids may be inserted, fully or partially, therein. For example, thereaction recesses may be sized and shaped to accommodate a capture bead.The capture bead may have clonally amplified DNA or other substancesthereon. Alternatively, the reaction recesses may be sized and shaped toreceive an approximate number of beads or solid substrates. As anotherexample, the reaction recesses may be filled with a porous gel orsubstance that is configured to control diffusion or filter fluids orsolutions that may flow into the reaction recesses.

In some examples, light sensors (e.g., photodiodes) are associated withcorresponding reaction sites. A light sensor that is associated with areaction site is configured to detect light emissions from theassociated reaction site via at least one light guide when a designatedreaction has occurred at the associated reaction site. In some cases, aplurality of light sensors (e.g. several pixels of a light detection orcamera device) may be associated with a single reaction site. In othercases, a single light sensor (e.g. a single pixel) may be associatedwith a single reaction site or with a group of reaction sites. The lightsensor, the reaction site, and other features of the biosensor may beconfigured so that at least some of the light is directly detected bythe light sensor without being reflected.

As used herein, a “biological or chemical substance” includesbiomolecules, samples-of-interest, analytes-of-interest, and otherchemical compound(s). A biological or chemical substance may be used todetect, identify, or analyze other chemical compound(s), or function asintermediaries to study or analyze other chemical compound(s). Inparticular examples, the biological or chemical substances include abiomolecule. As used herein, a “biomolecule” includes at least one of abiopolymer, nucleoside, nucleic acid, polynucleotide, oligonucleotide,protein, enzyme, polypeptide, antibody, antigen, ligand, receptor,polysaccharide, carbohydrate, polyphosphate, cell, tissue, organism, orfragment thereof or any other biologically active chemical compound(s)such as analogs or mimetics of the aforementioned species. In a furtherexample, a biological or chemical substance or a biomolecule includes anenzyme or reagent used in a coupled reaction to detect the product ofanother reaction such as an enzyme or reagent, such as an enzyme orreagent used to detect pyrophosphate in a pyrosequencing reaction.Enzymes and reagents useful for pyrophosphate detection are described,for example, in U.S. Patent Publication No. 2005/0244870 A1, which isincorporated by reference in its entirety.

Biomolecules, samples, and biological or chemical substances may benaturally occurring or synthetic and may be suspended in a solution ormixture within a reaction recess or region. Biomolecules, samples, andbiological or chemical substances may also be bound to a solid phase orgel material. Biomolecules, samples, and biological or chemicalsubstances may also include a pharmaceutical composition. In some cases,biomolecules, samples, and biological or chemical substances of interestmay be referred to as targets, probes, or analytes.

As used herein, a “biosensor” includes a device that includes a reactionstructure with a plurality of reaction sites that is configured todetect designated reactions that occur at or proximate to the reactionsites. A biosensor may include a solid-state light detection or“imaging” device (e.g., CCD or CMOS light detection device) and,optionally, a flow cell mounted thereto. The flow cell may include atleast one flow channel that is in fluid communication with the reactionsites. As one specific example, the biosensor is configured tofluidically and electrically couple to a bioassay system. The bioassaysystem may deliver a reaction solution to the reaction sites accordingto a predetermined protocol (e.g., sequencing-by-synthesis) and performa plurality of imaging events. For example, the bioassay system maydirect reaction solutions to flow along the reaction sites. At least oneof the reaction solutions may include four types of nucleotides havingthe same or different fluorescent labels. The nucleotides may bind tothe reaction sites, such as to corresponding oligonucleotides at thereaction sites. The bioassay system may then illuminate the reactionsites using an excitation light source (e.g., solid-state light sources,such as light-emitting diodes (LEDs)). The excitation light may have apredetermined wavelength or wavelengths, including a range ofwavelengths. The fluorescent labels excited by the incident excitationlight may provide emission signals (e.g., light of a wavelength orwavelengths that differ from the excitation light and, potentially, eachother) that may be detected by the light sensors.

As used herein, the term “immobilized,” when used with respect to abiomolecule or biological or chemical substance, includes substantiallyattaching the biomolecule or biological or chemical substance at amolecular level to a surface, such as to a detection surface of a lightdetection device or reaction structure. For example, a biomolecule orbiological or chemical substance may be immobilized to a surface of thereaction structure using adsorption techniques including non-covalentinteractions (e.g., electrostatic forces, van der Waals, and dehydrationof hydrophobic interfaces) and covalent binding techniques wherefunctional groups or linkers facilitate attaching the biomolecules tothe surface. Immobilizing biomolecules or biological or chemicalsubstances to the surface may be based upon the properties of thesurface, the liquid medium carrying the biomolecule or biological orchemical substance, and the properties of the biomolecules or biologicalor chemical substances themselves. In some cases, the surface may befunctionalized (e.g., chemically or physically modified) to facilitateimmobilizing the biomolecules (or biological or chemical substances) tothe surface.

In some examples, nucleic acids can be immobilized to the reactionstructure, such as to surfaces of reaction recesses thereof. Inparticular examples, the devices, biosensors, bioassay systems andmethods described herein may include the use of natural nucleotides andalso enzymes that are configured to interact with the naturalnucleotides. Natural nucleotides include, for example, ribonucleotidesor deoxyribonucleotides. Natural nucleotides can be in the mono-, di-,or tri-phosphate form and can have a base selected from adenine (A),Thymine (T), uracil (U), guanine (G) or cytosine (C). It will beunderstood, however, that non-natural nucleotides, modified nucleotidesor analogs of the aforementioned nucleotides can be used.

As noted above, a biomolecule or biological or chemical substance may beimmobilized at a reaction site in a reaction recess of a reactionstructure. Such a biomolecule or biological substance may be physicallyheld or immobilized within the reaction recesses through an interferencefit, adhesion, covalent bond, or entrapment. Examples of items or solidsthat may be disposed within the reaction recesses include polymer beads,pellets, agarose gel, powders, quantum dots, or other solids that may becompressed and/or held within the reaction chamber. In certainimplementations, the reaction recesses may be coated or filled with ahydrogel layer capable of covalently binding DNA oligonucleotides. Inparticular examples, a nucleic acid superstructure, such as a DNA ball,can be disposed in or at a reaction recess, for example, by attachmentto an interior surface of the reaction recess or by residence in aliquid within the reaction recess. A DNA ball or other nucleic acidsuperstructure can be performed and then disposed in or at a reactionrecess. Alternatively, a DNA ball can be synthesized in situ at areaction recess. A substance that is immobilized in a reaction recesscan be in a solid, liquid, or gaseous state.

As used herein, the term “analyte” is intended to mean a point or areain a pattern that can be distinguished from other points or areasaccording to relative location. An individual analyte can include one ormore molecules of a particular type. For example, an analyte can includea single target nucleic acid molecule having a particular sequence or ananalyte can include several nucleic acid molecules having the samesequence (and/or complementary sequence, thereof). Different moleculesthat are at different analytes of a pattern can be differentiated fromeach other according to the locations of the analytes in the pattern.Example analytes include without limitation, wells in a substrate, beads(or other particles) in or on a substrate, projections from a substrate,ridges on a substrate, pads of gel material on a substrate, or channelsin a substrate.

Any of a variety of target analytes that are to be detected,characterized, or identified can be used in an apparatus, system ormethod set forth herein. Exemplary analytes include, but are not limitedto, nucleic acids (e.g., DNA, RNA or analogs thereof), proteins,polysaccharides, cells, antibodies, epitopes, receptors, ligands,enzymes (e.g. kinases, phosphatases or polymerases), small molecule drugcandidates, cells, viruses, organisms, or the like.

The terms “analyte”, “nucleic acid”, “nucleic acid molecule”, and“polynucleotide” are used interchangeably herein. In variousimplementations, nucleic acids may be used as templates as providedherein (e.g., a nucleic acid template, or a nucleic acid complement thatis complementary to a nucleic acid nucleic acid template) for particulartypes of nucleic acid analysis, including but not limited to nucleicacid amplification, nucleic acid expression analysis, and/or nucleicacid sequence determination or suitable combinations thereof. Nucleicacids in certain implementations include, for instance, linear polymersof deoxyribonucleotides in 3′-5′ phosphodiester or other linkages, suchas deoxyribonucleic acids (DNA), for example, single- anddouble-stranded DNA, genomic DNA, copy DNA or complementary DNA (cDNA),recombinant DNA, or any form of synthetic or modified DNA. In otherimplementations, nucleic acids include for instance, linear polymers ofribonucleotides in 3′-5′ phosphodiester or other linkages such asribonucleic acids (RNA), for example, single- and double-stranded RNA,messenger (mRNA), copy RNA or complementary RNA (cRNA), alternativelyspliced mRNA, ribosomal RNA, small nucleolar RNA (snoRNA), microRNAs(miRNA), small interfering RNAs (sRNA), piwi RNAs (piRNA), or any formof synthetic or modified RNA. Nucleic acids used in the compositions andmethods of the present invention may vary in length and may be intact orfull-length molecules or fragments or smaller parts of larger nucleicacid molecules. In particular implementations, a nucleic acid may haveone or more detectable labels, as described elsewhere herein.

The terms “analyte”, “cluster”, “nucleic acid cluster”, “nucleic acidcolony”, and “DNA cluster” are used interchangeably and refer to aplurality of copies of a nucleic acid template and/or complementsthereof attached to a solid support. Typically and in certain preferredimplementations, the nucleic acid cluster comprises a plurality ofcopies of template nucleic acid and/or complements thereof, attached viatheir 5′ termini to the solid support. The copies of nucleic acidstrands making up the nucleic acid clusters may be in a single or doublestranded form. Copies of a nucleic acid template that are present in acluster can have nucleotides at corresponding positions that differ fromeach other, for example, due to presence of a label moiety. Thecorresponding positions can also contain analog structures havingdifferent chemical structure but similar Watson-Crick base-pairingproperties, such as is the case for uracil and thymine.

Colonies of nucleic acids can also be referred to as “nucleic acidclusters”. Nucleic acid colonies can optionally be created by clusteramplification or bridge amplification techniques as set forth in furtherdetail elsewhere herein. Multiple repeats of a target sequence can bepresent in a single nucleic acid molecule, such as a concatamer createdusing a rolling circle amplification procedure.

The nucleic acid clusters of the invention can have different shapes,sizes and densities depending on the conditions used. For example,clusters can have a shape that is substantially round, multi-sided,donut-shaped or ring-shaped. The diameter of a nucleic acid cluster canbe designed to be from about 0.2 μm to about 6 μm, about 0.3 μm to about4 μm, about 0.4 μm to about 3 μm, about 0.5 μm to about 2 μm, about 0.75μm to about 1.5 μm, or any intervening diameter. In a particularimplementation, the diameter of a nucleic acid cluster is about 0.5 μm,about 1 μm, about 1.5 μm, about 2 μm, about 2.5 μm, about 3 μm, about 4μm, about 5 μm, or about 6 μm. The diameter of a nucleic acid clustermay be influenced by a number of parameters, including, but not limitedto the number of amplification cycles performed in producing thecluster, the length of the nucleic acid template or the density ofprimers attached to the surface upon which clusters are formed. Thedensity of nucleic acid clusters can be designed to typically be in therange of 0.1/mm², 1/mm², 10/mm², 100/mm², 1,000/mm², 10,000/mm² to100,000/mm². The present invention further contemplates, in part, higherdensity nucleic acid clusters, for example, 100,000/mm² to 1,000,000/mm²and 1,000,000/mm² to 10,000,000/mm².

As used herein, an “analyte” is an area of interest within a specimen orfield of view. When used in connection with microarray devices or othermolecular analytical devices, an analyte refers to the area occupied bysimilar or identical molecules. For example, an analyte can be anamplified oligonucleotide or any other group of a polynucleotide orpolypeptide with a same or similar sequence. In other implementations,an analyte can be any element or group of elements that occupy aphysical area on a specimen. For example, an analyte could be a parcelof land, a body of water or the like. When an analyte is imaged, eachanalyte will have some area. Thus, in many implementations, an analyteis not merely one pixel.

The distances between analytes can be described in any number of ways.In some implementations, the distances between analytes can be describedfrom the center of one analyte to the center of another analyte. Inother implementations, the distances can be described from the edge ofone analyte to the edge of another analyte, or between the outer-mostidentifiable points of each analyte. The edge of an analyte can bedescribed as the theoretical or actual physical boundary on a chip, orsome point inside the boundary of the analyte. In other implementations,the distances can be described in relation to a fixed point on thespecimen or in the image of the specimen.

Generally several implementations will be described herein with respectto a method of analysis. It will be understood that systems are alsoprovided for carrying out the methods in an automated or semi-automatedway. Accordingly, this disclosure provides neural network-based templategeneration and base calling systems, wherein the systems can include aprocessor; a storage device; and a program for image analysis, theprogram including instructions for carrying out one or more of themethods set forth herein. Accordingly, the methods set forth herein canbe carried out on a computer, for example, having components set forthherein or otherwise known in the art.

The methods and systems set forth herein are useful for analyzing any ofa variety of objects. Particularly useful objects are solid supports orsolid-phase surfaces with attached analytes. The methods and systems setforth herein provide advantages when used with objects having arepeating pattern of analytes in an xy plane. An example is a microarrayhaving an attached collection of cells, viruses, nucleic acids,proteins, antibodies, carbohydrates, small molecules (such as drugcandidates), biologically active molecules or other analytes ofinterest.

An increasing number of applications have been developed for arrays withanalytes having biological molecules such as nucleic acids andpolypeptides. Such microarrays typically include deoxyribonucleic acid(DNA) or ribonucleic acid (RNA) probes. These are specific fornucleotide sequences present in humans and other organisms. In certainapplications, for example, individual DNA or RNA probes can be attachedat individual analytes of an array. A test sample, such as from a knownperson or organism, can be exposed to the array, such that targetnucleic acids (e.g., gene fragments, mRNA, or amplicons thereof)hybridize to complementary probes at respective analytes in the array.The probes can be labeled in a target specific process (e.g., due tolabels present on the target nucleic acids or due to enzymatic labelingof the probes or targets that are present in hybridized form at theanalytes). The array can then be examined by scanning specificfrequencies of light over the analytes to identify which target nucleicacids are present in the sample.

Biological microarrays may be used for genetic sequencing and similarapplications. In general, genetic sequencing comprises determining theorder of nucleotides in a length of target nucleic acid, such as afragment of DNA or RNA. Relatively short sequences are typicallysequenced at each analyte, and the resulting sequence information may beused in various bioinformatics methods to logically fit the sequencefragments together so as to reliably determine the sequence of much moreextensive lengths of genetic material from which the fragments werederived. Automated, computer-based algorithms for characteristicfragments have been developed, and have been used more recently ingenome mapping, identification of genes and their function, and soforth. Microarrays are particularly useful for characterizing genomiccontent because a large number of variants are present and thissupplants the alternative of performing many experiments on individualprobes and targets. The microarray is an ideal format for performingsuch investigations in a practical manner.

Any of a variety of analyte arrays (also referred to as “microarrays”)known in the art can be used in a method or system set forth herein. Atypical array contains analytes, each having an individual probe or apopulation of probes. In the latter case, the population of probes ateach analyte is typically homogenous having a single species of probe.For example, in the case of a nucleic acid array, each analyte can havemultiple nucleic acid molecules each having a common sequence. However,in some implementations the populations at each analyte of an array canbe heterogeneous. Similarly, protein arrays can have analytes with asingle protein or a population of proteins typically, but not always,having the same amino acid sequence. The probes can be attached to thesurface of an array for example, via covalent linkage of the probes tothe surface or via non-covalent interaction(s) of the probes with thesurface. In some implementations, probes, such as nucleic acidmolecules, can be attached to a surface via a gel layer as described,for example, in U.S. patent application Ser. No. 13/784,368 and US Pat.App. Pub. No. 2011/0059865 A1, each of which is incorporated herein byreference.

Example arrays include, without limitation, a BeadChip Array availablefrom Illumina, Inc. (San Diego, Calif.) or others such as those whereprobes are attached to beads that are present on a surface (e.g. beadsin wells on a surface) such as those described in U.S. Pat. Nos.6,266,459; 6,355,431; 6,770,441; 6,859,570; or 7,622,294; or PCTPublication No. WO 00/63437, each of which is incorporated herein byreference. Further examples of commercially available microarrays thatcan be used include, for example, an Affymetrix® GeneChip® microarray orother microarray synthesized in accordance with techniques sometimesreferred to as VLSIPS™ (Very Large Scale Immobilized Polymer Synthesis)technologies. A spotted microarray can also be used in a method orsystem according to some implementations of the present disclosure. Anexample spotted microarray is a CodeLink™ Array available from AmershamBiosciences. Another microarray that is useful is one that ismanufactured using inkjet printing methods such as SurePrint™ Technologyavailable from Agilent Technologies.

Other useful arrays include those that are used in nucleic acidsequencing applications. For example, arrays having amplicons of genomicfragments (often referred to as clusters) are particularly useful suchas those described in Bentley et al., Nature 456:53-59 (2008), WO04/018497; WO 91/06678; WO 07/123744; U.S. Pat. Nos. 7,329,492;7,211,414; 7,315,019; 7,405,281, or 7,057,026; or US Pat. App. Pub. No.2008/0108082 A1, each of which is incorporated herein by reference.Another type of array that is useful for nucleic acid sequencing is anarray of particles produced from an emulsion PCR technique. Examples aredescribed in Dressman et al., Proc. Natl. Acad. Sci. USA 100:8817-8822(2003), WO 05/010145, US Pat. App. Pub. No. 2005/0130173 or US Pat. App.Pub. No. 2005/0064460, each of which is incorporated herein by referencein its entirety.

Arrays used for nucleic acid sequencing often have random spatialpatterns of nucleic acid analytes. For example, HiSeq or MiSeqsequencing platforms available from Illumina Inc. (San Diego, Calif.)utilize flow cells upon which nucleic acid arrays are formed by randomseeding followed by bridge amplification. However, patterned arrays canalso be used for nucleic acid sequencing or other analyticalapplications. Example patterned arrays, methods for their manufactureand methods for their use are set forth in U.S. Ser. No. 13/787,396;U.S. Ser. No. 13/783,043; U.S. Ser. No. 13/784,368; US Pat. App. Pub.No. 2013/0116153 A1; and US Pat. App. Pub. No. 2012/0316086 A1, each ofwhich is incorporated herein by reference. The analytes of suchpatterned arrays can be used to capture a single nucleic acid templatemolecule to seed subsequent formation of a homogenous colony, forexample, via bridge amplification. Such patterned arrays areparticularly useful for nucleic acid sequencing applications.

The size of an analyte on an array (or other object used in a method orsystem herein) can be selected to suit a particular application. Forexample, in some implementations, an analyte of an array can have a sizethat accommodates only a single nucleic acid molecule. A surface havinga plurality of analytes in this size range is useful for constructing anarray of molecules for detection at single molecule resolution. Analytesin this size range are also useful for use in arrays having analytesthat each contain a colony of nucleic acid molecules. Thus, the analytesof an array can each have an area that is no larger than about 1 mm², nolarger than about 500 μm², no larger than about 100 μm², no larger thanabout 10 μm², no larger than about 1 μm², no larger than about 500 nm²,or no larger than about 100 nm², no larger than about 10 nm², no largerthan about 5 nm², or no larger than about 1 nm². Alternatively oradditionally, the analytes of an array will be no smaller than about 1mm², no smaller than about 500 μm², no smaller than about 100 μm², nosmaller than about 10 μm², no smaller than about 1 μm², no smaller thanabout 500 nm², no smaller than about 100 nm², no smaller than about 10nm², no smaller than about 5 nm², or no smaller than about 1 nm².Indeed, an analyte can have a size that is in a range between an upperand lower limit selected from those exemplified above. Although severalsize ranges for analytes of a surface have been exemplified with respectto nucleic acids and on the scale of nucleic acids, it will beunderstood that analytes in these size ranges can be used forapplications that do not include nucleic acids. It will be furtherunderstood that the size of the analytes need not necessarily beconfined to a scale used for nucleic acid applications.

For implementations that include an object having a plurality ofanalytes, such as an array of analytes, the analytes can be discrete,being separated with spaces between each other. An array useful in theinvention can have analytes that are separated by edge to edge distanceof at most 100 μm, 50 μm, 10 μm, 5 μm, 1 μm, 0.5 μm, or less.Alternatively or additionally, an array can have analytes that areseparated by an edge to edge distance of at least 0.5 μm, 1 μm, 5 μm, 10μm, 50 μm, 100 μm, or more. These ranges can apply to the average edgeto edge spacing for analytes as well as to the minimum or maximumspacing.

In some implementations the analytes of an array need not be discreteand instead neighboring analytes can abut each other. Whether or not theanalytes are discrete, the size of the analytes and/or pitch of theanalytes can vary such that arrays can have a desired density. Forexample, the average analyte pitch in a regular pattern can be at most100 μm, 50 μm, 10 μm, 5 μm, 1 μm, 0.5 μm, or less. Alternatively oradditionally, the average analyte pitch in a regular pattern can be atleast 0.5 μm, 1 μm, 5 μm, 10 μm, 50 μm, 100 μm, or more. These rangescan apply to the maximum or minimum pitch for a regular pattern as well.For example, the maximum analyte pitch for a regular pattern can be atmost 100 μm, 50 μm, 10 μm, 5 μm, 1 μm, 0.5 μm, or less; and/or theminimum analyte pitch in a regular pattern can be at least 0.5 μm, 1 μm,5 μm, 10 μm, 50 μm, 100 μm, or more.

The density of analytes in an array can also be understood in terms ofthe number of analytes present per unit area. For example, the averagedensity of analytes for an array can be at least about 1×10³analytes/mm², 1×10⁴ analytes/mm², 1×10⁵ analytes/mm², 1×10⁶analytes/mm², 1×10⁷ analytes/mm², 1×10⁸ analytes/mm², or 1×10⁹analytes/mm², or higher. Alternatively or additionally the averagedensity of analytes for an array can be at most about 1×10⁹analytes/mm², 1×10⁸ analytes/mm², 1×10⁷ analytes/mm², 1×10⁶analytes/mm², 1×10⁵ analytes/mm², 1×10⁴ analytes/mm², or 1×10³analytes/mm², or less.

The above ranges can apply to all or part of a regular patternincluding, for example, all or part of an array of analytes.

The analytes in a pattern can have any of a variety of shapes. Forexample, when observed in a two dimensional plane, such as on thesurface of an array, the analytes can appear rounded, circular, oval,rectangular, square, symmetric, asymmetric, triangular, polygonal, orthe like. The analytes can be arranged in a regular repeating patternincluding, for example, a hexagonal or rectilinear pattern. A patterncan be selected to achieve a desired level of packing. For example,round analytes are optimally packed in a hexagonal arrangement. Ofcourse other packing arrangements can also be used for round analytesand vice versa.

A pattern can be characterized in terms of the number of analytes thatare present in a subset that forms the smallest geometric unit of thepattern. The subset can include, for example, at least about 2, 3, 4, 5,6, 10 or more analytes. Depending upon the size and density of theanalytes the geometric unit can occupy an area of less than 1 mm², 500μm², 100 μm², 50 μm², 10 μm², 1 μm², 500 nm², 100 nm², 50 nm², 10 nm²,or less. Alternatively or additionally, the geometric unit can occupy anarea of greater than 10 nm², 50 nm², 100 nm², 500 nm², 1 μm², 10 μm², 50μm², 100 μm², 500 μm², 1 mm², or more. Characteristics of the analytesin a geometric unit, such as shape, size, pitch and the like, can beselected from those set forth herein more generally with regard toanalytes in an array or pattern.

An array having a regular pattern of analytes can be ordered withrespect to the relative locations of the analytes but random withrespect to one or more other characteristic of each analyte. Forexample, in the case of a nucleic acid array, the nuclei acid analytescan be ordered with respect to their relative locations but random withrespect to one's knowledge of the sequence for the nucleic acid speciespresent at any particular analyte. As a more specific example, nucleicacid arrays formed by seeding a repeating pattern of analytes withtemplate nucleic acids and amplifying the template at each analyte toform copies of the template at the analyte (e.g., via clusteramplification or bridge amplification) will have a regular pattern ofnucleic acid analytes but will be random with regard to the distributionof sequences of the nucleic acids across the array. Thus, detection ofthe presence of nucleic acid material generally on the array can yield arepeating pattern of analytes, whereas sequence specific detection canyield non-repeating distribution of signals across the array.

It will be understood that the description herein of patterns, order,randomness and the like pertain not only to analytes on objects, such asanalytes on arrays, but also to analytes in images. As such, patterns,order, randomness and the like can be present in any of a variety offormats that are used to store, manipulate or communicate image dataincluding, but not limited to, a computer readable medium or computercomponent such as a graphical user interface or other output device.

As used herein, the term “image” is intended to mean a representation ofall or part of an object. The representation can be an opticallydetected reproduction. For example, an image can be obtained fromfluorescent, luminescent, scatter, or absorption signals. The part ofthe object that is present in an image can be the surface or other xyplane of the object. Typically, an image is a 2 dimensionalrepresentation, but in some cases information in the image can bederived from 3 or more dimensions. An image need not include opticallydetected signals. Non-optical signals can be present instead. An imagecan be provided in a computer readable format or medium such as one ormore of those set forth elsewhere herein.

As used herein, “image” refers to a reproduction or representation of atleast a portion of a specimen or other object. In some implementations,the reproduction is an optical reproduction, for example, produced by acamera or other optical detector. The reproduction can be a non-opticalreproduction, for example, a representation of electrical signalsobtained from an array of nanopore analytes or a representation ofelectrical signals obtained from an ion-sensitive CMOS detector. Inparticular implementations non-optical reproductions can be excludedfrom a method or apparatus set forth herein. An image can have aresolution capable of distinguishing analytes of a specimen that arepresent at any of a variety of spacings including, for example, thosethat are separated by less than 100 μm, 50 μm, 10 μm, 5 μm, 1 μm or 0.5μm.

As used herein, “acquiring”, “acquisition” and like terms refer to anypart of the process of obtaining an image file. In some implementations,data acquisition can include generating an image of a specimen, lookingfor a signal in a specimen, instructing a detection device to look foror generate an image of a signal, giving instructions for furtheranalysis or transformation of an image file, and any number oftransformations or manipulations of an image file.

As used herein, the term “template” refers to a representation of thelocation or relation between signals or analytes. Thus, in someimplementations, a template is a physical grid with a representation ofsignals corresponding to analytes in a specimen. In someimplementations, a template can be a chart, table, text file or othercomputer file indicative of locations corresponding to analytes. Inimplementations presented herein, a template is generated in order totrack the location of analytes of a specimen across a set of images ofthe specimen captured at different reference points. For example, atemplate could be a set of x,y coordinates or a set of values thatdescribe the direction and/or distance of one analyte with respect toanother analyte.

As used herein, the term “specimen” can refer to an object or area of anobject of which an image is captured. For example, in implementationswhere images are taken of the surface of the earth, a parcel of land canbe a specimen. In other implementations where the analysis of biologicalmolecules is performed in a flow cell, the flow cell may be divided intoany number of subdivisions, each of which may be a specimen. Forexample, a flow cell may be divided into various flow channels or lanes,and each lane can be further divided into 2, 3, 4, 5, 6, 7, 8, 9, 10,20, 30, 40, 50, 60 70, 80, 90, 100, 110, 120, 140, 160, 180, 200, 400,600, 800, 1000 or more separate regions that are imaged. One example ofa flow cell has 8 lanes, with each lane divided into 120 specimens ortiles. In another implementation, a specimen may be made up of aplurality of tiles or even an entire flow cell. Thus, the image of eachspecimen can represent a region of a larger surface that is imaged.

It will be appreciated that references to ranges and sequential numberlists described herein include not only the enumerated number but allreal numbers between the enumerated numbers.

As used herein, a “reference point” refers to any temporal or physicaldistinction between images. In a preferred implementation, a referencepoint is a time point. In a more preferred implementation, a referencepoint is a time point or cycle during a sequencing reaction. However,the term “reference point” can include other aspects that distinguish orseparate images, such as angle, rotational, temporal, or other aspectsthat can distinguish or separate images.

As used herein, a “subset of images” refers to a group of images withina set. For example, a subset may contain 1, 2, 3, 4, 6, 8, 10, 12, 14,16, 18, 20, 30, 40, 50, 60 or any number of images selected from a setof images. In particular implementations, a subset may contain no morethan 1, 2, 3, 4, 6, 8, 10, 12, 14, 16, 18, 20, 30, 40, 50, 60 or anynumber of images selected from a set of images. In a preferredimplementation, images are obtained from one or more sequencing cycleswith four images correlated to each cycle. Thus, for example, a subsetcould be a group of 16 images obtained through four cycles.

A base refers to a nucleotide base or nucleotide, A (adenine), C(cytosine), T (thymine), or G (guanine). This application uses “base(s)”and “nucleotide(s)” interchangeably.

The term “chromosome” refers to the heredity-bearing gene carrier of aliving cell, which is derived from chromatin strands comprising DNA andprotein components (especially histones). The conventionalinternationally recognized individual human genome chromosome numberingsystem is employed herein.

The term “site” refers to a unique position (e.g., chromosome ID,chromosome position and orientation) on a reference genome. In someimplementations, a site may be a residue, a sequence tag, or a segment'sposition on a sequence. The term “locus” may be used to refer to thespecific location of a nucleic acid sequence or polymorphism on areference chromosome.

The term “sample” herein refers to a sample, typically derived from abiological fluid, cell, tissue, organ, or organism containing a nucleicacid or a mixture of nucleic acids containing at least one nucleic acidsequence that is to be sequenced and/or phased. Such samples include,but are not limited to sputum/oral fluid, amniotic fluid, blood, a bloodfraction, fine needle biopsy samples (e.g., surgical biopsy, fine needlebiopsy, etc.), urine, peritoneal fluid, pleural fluid, tissue explant,organ culture and any other tissue or cell preparation, or fraction orderivative thereof or isolated therefrom. Although the sample is oftentaken from a human subject (e.g., patient), samples can be taken fromany organism having chromosomes, including, but not limited to dogs,cats, horses, goats, sheep, cattle, pigs, etc. The sample may be useddirectly as obtained from the biological source or following apretreatment to modify the character of the sample. For example, suchpretreatment may include preparing plasma from blood, diluting viscousfluids and so forth. Methods of pretreatment may also involve, but arenot limited to, filtration, precipitation, dilution, distillation,mixing, centrifugation, freezing, lyophilization, concentration,amplification, nucleic acid fragmentation, inactivation of interferingcomponents, the addition of reagents, lysing, etc.

The term “sequence” includes or represents a strand of nucleotidescoupled to each other. The nucleotides may be based on DNA or RNA. Itshould be understood that one sequence may include multiplesub-sequences. For example, a single sequence (e.g., of a PCR amplicon)may have 350 nucleotides. The sample read may include multiplesub-sequences within these 350 nucleotides. For instance, the sampleread may include first and second flanking subsequences having, forexample, 20-50 nucleotides. The first and second flanking sub-sequencesmay be located on either side of a repetitive segment having acorresponding sub-sequence (e.g., 40-100 nucleotides). Each of theflanking sub-sequences may include (or include portions of) a primersub-sequence (e.g., 10-30 nucleotides). For ease of reading, the term“sub-sequence” will be referred to as “sequence,” but it is understoodthat two sequences are not necessarily separate from each other on acommon strand. To differentiate the various sequences described herein,the sequences may be given different labels (e.g., target sequence,primer sequence, flanking sequence, reference sequence, and the like).Other terms, such as “allele,” may be given different labels todifferentiate between like objects. The application uses “read(s)” and“sequence read(s)” interchangeably.

The term “paired-end sequencing” refers to sequencing methods thatsequence both ends of a target fragment. Paired-end sequencing mayfacilitate detection of genomic rearrangements and repetitive segments,as well as gene fusions and novel transcripts. Methodology forpaired-end sequencing are described in PCT publication WO07010252, PCTapplication Serial No. PCTGB2007/003798 and US patent applicationpublication US 2009/0088327, each of which is incorporated by referenceherein. In one example, a series of operations may be performed asfollows; (a) generate clusters of nucleic acids; (b) linearize thenucleic acids; (c) hybridize a first sequencing primer and carry outrepeated cycles of extension, scanning and deblocking, as set forthabove; (d) “invert” the target nucleic acids on the flow cell surface bysynthesizing a complimentary copy; (e) linearize the resynthesizedstrand; and (f) hybridize a second sequencing primer and carry outrepeated cycles of extension, scanning and deblocking, as set forthabove. The inversion operation can be carried out be delivering reagentsas set forth above for a single cycle of bridge amplification.

The term “reference genome” or “reference sequence” refers to anyparticular known genome sequence, whether partial or complete, of anyorganism which may be used to reference identified sequences from asubject. For example, a reference genome used for human subjects as wellas many other organisms is found at the National Center forBiotechnology Information at ncbi.nlm.nih.gov. A “genome” refers to thecomplete genetic information of an organism or virus, expressed innucleic acid sequences. A genome includes both the genes and thenoncoding sequences of the DNA. The reference sequence may be largerthan the reads that are aligned to it. For example, it may be at leastabout 100 times larger, or at least about 1000 times larger, or at leastabout 10,000 times larger, or at least about 105 times larger, or atleast about 106 times larger, or at least about 107 times larger. In oneexample, the reference genome sequence is that of a full length humangenome. In another example, the reference genome sequence is limited toa specific human chromosome such as chromosome 13. In someimplementations, a reference chromosome is a chromosome sequence fromhuman genome version hg19. Such sequences may be referred to aschromosome reference sequences, although the term reference genome isintended to cover such sequences. Other examples of reference sequencesinclude genomes of other species, as well as chromosomes,sub-chromosomal regions (such as strands), etc., of any species. Invarious implementations, the reference genome is a consensus sequence orother combination derived from multiple individuals. However, in certainapplications, the reference sequence may be taken from a particularindividual. In other implementations, the “genome” also covers so-called“graph genomes”, which use a particular storage format andrepresentation of the genome sequence. In one implementation, graphgenomes store data in a linear file. In another implementation, thegraph genomes refer to a representation where alternative sequences(e.g., different copies of a chromosome with small differences) arestored as different paths in a graph. Additional information regardinggraph genome implementations can be found inhttps://www.biorxiv.org/content/biorxiv/early/2018/03/20/194530.full.pdf,the content of which is hereby incorporated herein by reference in itsentirety.

The term “read” refer to a collection of sequence data that describes afragment of a nucleotide sample or reference. The term “read” may referto a sample read and/or a reference read. Typically, though notnecessarily, a read represents a short sequence of contiguous base pairsin the sample or reference. The read may be represented symbolically bythe base pair sequence (in ATCG) of the sample or reference fragment. Itmay be stored in a memory device and processed as appropriate todetermine whether the read matches a reference sequence or meets othercriteria. A read may be obtained directly from a sequencing apparatus orindirectly from stored sequence information concerning the sample. Insome cases, a read is a DNA sequence of sufficient length (e.g., atleast about 25 bp) that can be used to identify a larger sequence orregion, e.g., that can be aligned and specifically assigned to achromosome or genomic region or gene.

Next-generation sequencing methods include, for example, sequencing bysynthesis technology (Illumina), pyrosequencing (454), ion semiconductortechnology (Ion Torrent sequencing), single-molecule real-timesequencing (Pacific Biosciences) and sequencing by ligation (SOLiDsequencing). Depending on the sequencing methods, the length of eachread may vary from about 30 bp to more than 10,000 bp. For example, theDNA sequencing method using SOLiD sequencer generates nucleic acid readsof about 50 bp. For another example, Ion Torrent Sequencing generatesnucleic acid reads of up to 400 bp and 454 pyrosequencing generatesnucleic acid reads of about 700 bp. For yet another example,single-molecule real-time sequencing methods may generate reads of10,000 bp to 15,000 bp. Therefore, in certain implementations, thenucleic acid sequence reads have a length of 30-100 bp, 50-200 bp, or50-400 bp.

The terms “sample read”, “sample sequence” or “sample fragment” refer tosequence data for a genomic sequence of interest from a sample. Forexample, the sample read comprises sequence data from a PCR ampliconhaving a forward and reverse primer sequence. The sequence data can beobtained from any select sequence methodology. The sample read can be,for example, from a sequencing-by-synthesis (SBS) reaction, asequencing-by-ligation reaction, or any other suitable sequencingmethodology for which it is desired to determine the length and/oridentity of a repetitive element. The sample read can be a consensus(e.g., averaged or weighted) sequence derived from multiple samplereads. In certain implementations, providing a reference sequencecomprises identifying a locus-of-interest based upon the primer sequenceof the PCR amplicon.

The term “raw fragment” refers to sequence data for a portion of agenomic sequence of interest that at least partially overlaps adesignated position or secondary position of interest within a sampleread or sample fragment. Non-limiting examples of raw fragments includea duplex stitched fragment, a simplex stitched fragment, a duplexun-stitched fragment and a simplex un-stitched fragment. The term “raw”is used to indicate that the raw fragment includes sequence data havingsome relation to the sequence data in a sample read, regardless ofwhether the raw fragment exhibits a supporting variant that correspondsto and authenticates or confirms a potential variant in a sample read.The term “raw fragment” does not indicate that the fragment necessarilyincludes a supporting variant that validates a variant call in a sampleread. For example, when a sample read is determined by a variant callapplication to exhibit a first variant, the variant call application maydetermine that one or more raw fragments lack a corresponding type of“supporting” variant that may otherwise be expected to occur given thevariant in the sample read.

The terms “mapping”, “aligned,” “alignment,” or “aligning” refer to theprocess of comparing a read or tag to a reference sequence and therebydetermining whether the reference sequence contains the read sequence.If the reference sequence contains the read, the read may be mapped tothe reference sequence or, in certain implementations, to a particularlocation in the reference sequence. In some cases, alignment simplytells whether or not a read is a member of a particular referencesequence (i.e., whether the read is present or absent in the referencesequence). For example, the alignment of a read to the referencesequence for human chromosome 13 will tell whether the read is presentin the reference sequence for chromosome 13. A tool that provides thisinformation may be called a set membership tester. In some cases, analignment additionally indicates a location in the reference sequencewhere the read or tag maps to. For example, if the reference sequence isthe whole human genome sequence, an alignment may indicate that a readis present on chromosome 13, and may further indicate that the read ison a particular strand and/or site of chromosome 13.

The term “indel” refers to the insertion and/or the deletion of bases inthe DNA of an organism. A micro-indel represents an indel that resultsin a net change of 1 to 50 nucleotides. In coding regions of the genome,unless the length of an indel is a multiple of 3, it will produce aframeshift mutation. Indels can be contrasted with point mutations. Anindel inserts and deletes nucleotides from a sequence, while a pointmutation is a form of substitution that replaces one of the nucleotideswithout changing the overall number in the DNA. Indels can also becontrasted with a Tandem Base Mutation (TBM), which may be defined assubstitution at adjacent nucleotides (primarily substitutions at twoadjacent nucleotides, but substitutions at three adjacent nucleotideshave been observed.

The term “variant” refers to a nucleic acid sequence that is differentfrom a nucleic acid reference. Typical nucleic acid sequence variantincludes without limitation single nucleotide polymorphism (SNP), shortdeletion and insertion polymorphisms (Indel), copy number variation(CNV), microsatellite markers or short tandem repeats and structuralvariation. Somatic variant calling is the effort to identify variantspresent at low frequency in the DNA sample. Somatic variant calling isof interest in the context of cancer treatment. Cancer is caused by anaccumulation of mutations in DNA. A DNA sample from a tumor is generallyheterogeneous, including some normal cells, some cells at an early stageof cancer progression (with fewer mutations), and some late-stage cells(with more mutations). Because of this heterogeneity, when sequencing atumor (e.g., from an FFPE sample), somatic mutations will often appearat a low frequency. For example, a SNV might be seen in only 10% of thereads covering a given base. A variant that is to be classified assomatic or germline by the variant classifier is also referred to hereinas the “variant under test”.

The term “noise” refers to a mistaken variant call resulting from one ormore errors in the sequencing process and/or in the variant callapplication.

The term “variant frequency” represents the relative frequency of anallele (variant of a gene) at a particular locus in a population,expressed as a fraction or percentage. For example, the fraction orpercentage may be the fraction of all chromosomes in the population thatcarry that allele. By way of example, sample variant frequencyrepresents the relative frequency of an allele/variant at a particularlocus/position along a genomic sequence of interest over a “population”corresponding to the number of reads and/or samples obtained for thegenomic sequence of interest from an individual. As another example, abaseline variant frequency represents the relative frequency of anallele/variant at a particular locus/position along one or more baselinegenomic sequences where the “population” corresponding to the number ofreads and/or samples obtained for the one or more baseline genomicsequences from a population of normal individuals.

The term “variant allele frequency (VAF)” refers to the percentage ofsequenced reads observed matching the variant divided by the overallcoverage at the target position. VAF is a measure of the proportion ofsequenced reads carrying the variant.

The terms “position”, “designated position”, and “locus” refer to alocation or coordinate of one or more nucleotides within a sequence ofnucleotides. The terms “position”, “designated position”, and “locus”also refer to a location or coordinate of one or more base pairs in asequence of nucleotides.

The term “haplotype” refers to a combination of alleles at adjacentsites on a chromosome that are inherited together. A haplotype may beone locus, several loci, or an entire chromosome depending on the numberof recombination events that have occurred between a given set of loci,if any occurred.

The term “threshold” herein refers to a numeric or non-numeric valuethat is used as a cutoff to characterize a sample, a nucleic acid, orportion thereof (e.g., a read). A threshold may be varied based uponempirical analysis. The threshold may be compared to a measured orcalculated value to determine whether the source giving rise to suchvalue suggests should be classified in a particular manner. Thresholdvalues can be identified empirically or analytically. The choice of athreshold is dependent on the level of confidence that the user wishesto have to make the classification. The threshold may be chosen for aparticular purpose (e.g., to balance sensitivity and selectivity). Asused herein, the term “threshold” indicates a point at which a course ofanalysis may be changed and/or a point at which an action may betriggered. A threshold is not required to be a predetermined number.Instead, the threshold may be, for instance, a function that is based ona plurality of factors. The threshold may be adaptive to thecircumstances. Moreover, a threshold may indicate an upper limit, alower limit, or a range between limits.

In some implementations, a metric or score that is based on sequencingdata may be compared to the threshold. As used herein, the terms“metric” or “score” may include values or results that were determinedfrom the sequencing data or may include functions that are based on thevalues or results that were determined from the sequencing data. Like athreshold, the metric or score may be adaptive to the circumstances. Forinstance, the metric or score may be a normalized value. As an exampleof a score or metric, one or more implementations may use count scoreswhen analyzing the data. A count score may be based on number of samplereads. The sample reads may have undergone one or more filtering stagessuch that the sample reads have at least one common characteristic orquality. For example, each of the sample reads that are used todetermine a count score may have been aligned with a reference sequenceor may be assigned as a potential allele. The number of sample readshaving a common characteristic may be counted to determine a read count.Count scores may be based on the read count. In some implementations,the count score may be a value that is equal to the read count. In otherimplementations, the count score may be based on the read count andother information. For example, a count score may be based on the readcount for a particular allele of a genetic locus and a total number ofreads for the genetic locus. In some implementations, the count scoremay be based on the read count and previously-obtained data for thegenetic locus. In some implementations, the count scores may benormalized scores between predetermined values. The count score may alsobe a function of read counts from other loci of a sample or a functionof read counts from other samples that were concurrently run with thesample-of-interest. For instance, the count score may be a function ofthe read count of a particular allele and the read counts of other lociin the sample and/or the read counts from other samples. As one example,the read counts from other loci and/or the read counts from othersamples may be used to normalize the count score for the particularallele.

The terms “coverage” or “fragment coverage” refer to a count or othermeasure of a number of sample reads for the same fragment of a sequence.A read count may represent a count of the number of reads that cover acorresponding fragment. Alternatively, the coverage may be determined bymultiplying the read count by a designated factor that is based onhistorical knowledge, knowledge of the sample, knowledge of the locus,etc.

The term “read depth” (conventionally a number followed by “×”) refersto the number of sequenced reads with overlapping alignment at thetarget position. This is often expressed as an average or percentageexceeding a cutoff over a set of intervals (such as exons, genes, orpanels). For example, a clinical report might say that a panel averagecoverage is 1,105× with 98% of targeted bases covered >100.

The terms “base call quality score” or “Q score” refer to a PHRED-scaledprobability ranging from 0-50 inversely proportional to the probabilitythat a single sequenced base is correct. For example, a T base call withQ of 20 is considered likely correct with a probability of 99.99%. Anybase call with Q<20 should be considered low quality, and any variantidentified where a substantial proportion of sequenced reads supportingthe variant are of low quality should be considered potentially falsepositive.

The terms “variant reads” or “variant read number” refer to the numberof sequenced reads supporting the presence of the variant.

Regarding “strandedness” (or DNA strandedness), the genetic message inDNA can be represented as a string of the letters A, G, C, and T. Forexample, 5′-AGGACA-3′. Often, the sequence is written in the directionshown here, i.e., with the 5′ end to the left and the 3′ end to theright. DNA may sometimes occur as single-stranded molecule (as incertain viruses), but normally we find DNA as a double-stranded unit. Ithas a double helical structure with two antiparallel strands. In thiscase, the word “antiparallel” means that the two strands run inparallel, but have opposite polarity. The double-stranded DNA is heldtogether by pairing between bases and the pairing is always such thatadenine (A) pairs with thymine (T) and cytosine (C) pairs with guanine(G). This pairing is referred to as complementarity, and one strand ofDNA is said to be the complement of the other. The double-stranded DNAmay thus be represented as two strings, like this: 5′-AGGACA-3′ and3′-TCCTGT-5′. Note that the two strands have opposite polarity.Accordingly, the strandedness of the two DNA strands can be referred toas the reference strand and its complement, forward and reverse strands,top and bottom strands, sense and antisense strands, or Watson and Crickstrands.

The reads alignment (also called reads mapping) is the process offiguring out where in the genome a sequence is from. Once the alignmentis performed, the “mapping quality” or the “mapping quality score(MAPQ)” of a given read quantifies the probability that its position onthe genome is correct. The mapping quality is encoded in the phred scalewhere P is the probability that the alignment is not correct. Theprobability is calculated as: P=10^((−MAQ/10)), where MAPQ is themapping quality. For example, a mapping quality of 40=10 to the power of−4, meaning that there is a 0.01% chance that the read was alignedincorrectly. The mapping quality is therefore associated with severalalignment factors, such as the base quality of the read, the complexityof the reference genome, and the paired-end information. Regarding thefirst, if the base quality of the read is low, it means that theobserved sequence might be wrong and thus its alignment is wrong.Regarding the second, the mappability refers to the complexity of thegenome. Repeated regions are more difficult to map and reads falling inthese regions usually get low mapping quality. In this context, the MAPQreflects the fact that the reads are not uniquely aligned and that theirreal origin cannot be determined. Regarding the third, in case ofpaired-end sequencing data, concordant pairs are more likely to be wellaligned. The higher is the mapping quality, the better is the alignment.A read aligned with a good mapping quality usually means that the readsequence was good and was aligned with few mismatches in a highmappability region. The MAPQ value can be used as a quality control ofthe alignment results. The proportion of reads aligned with an MAPQhigher than 20 is usually for downstream analysis.

As used herein, a “signal” refers to a detectable event such as anemission, preferably light emission, for example, in an image. Thus, inpreferred implementations, a signal can represent any detectable lightemission that is captured in an image (i.e., a “spot”). Thus, as usedherein, “signal” can refer to both an actual emission from an analyte ofthe specimen, and can refer to a spurious emission that does notcorrelate to an actual analyte. Thus, a signal could arise from noiseand could be later discarded as not representative of an actual analyteof a specimen.

As used herein, the term “clump” refers to a group of signals. Inparticular implementations, the signals are derived from differentanalytes. In a preferred implementation, a signal clump is a group ofsignals that cluster together. In a more preferred implementation, asignal clump represents a physical region covered by one amplifiedoligonucleotide. Each signal clump should be ideally observed as severalsignals (one per template cycle, and possibly more due to cross-talk).Accordingly, duplicate signals are detected where two (or more) signalsare included in a template from the same clump of signals.

As used herein, terms such as “minimum,” “maximum,” “minimize,”“maximize” and grammatical variants thereof can include values that arenot the absolute maxima or minima. In some implementations, the valuesinclude near maximum and near minimum values. In other implementations,the values can include local maximum and/or local minimum values. Insome implementations, the values include only absolute maximum orminimum values.

As used herein, “cross-talk” refers to the detection of signals in oneimage that are also detected in a separate image. In a preferredimplementation, cross-talk can occur when an emitted signal is detectedin two separate detection channels. For example, where an emitted signaloccurs in one color, the emission spectrum of that signal may overlapwith another emitted signal in another color. In a preferredimplementation, fluorescent molecules used to indicate the presence ofnucleotide bases A, C, G and T are detected in separate channels.However, because the emission spectra of A and C overlap, some of the Ccolor signal may be detected during detection using the A color channel.Accordingly, cross-talk between the A and C signals allows signals fromone color image to appear in the other color image. In someimplementations, G and T cross-talk. In some implementations, the amountof cross-talk between channels is asymmetric. It will be appreciatedthat the amount of cross-talk between channels can be controlled by,among other things, the selection of signal molecules having anappropriate emission spectrum as well as selection of the size andwavelength range of the detection channel.

As used herein, “register”, “registering”, “registration” and like termsrefer to any process to correlate signals in an image or data set from afirst time point or perspective with signals in an image or data setfrom another time point or perspective. For example, registration can beused to align signals from a set of images to form a template. Inanother example, registration can be used to align signals from otherimages to a template. One signal may be directly or indirectlyregistered to another signal. For example, a signal from image “S” maybe registered to image “G” directly. As another example, a signal fromimage “N” may be directly registered to image “G”, or alternatively, thesignal from image “N” may be registered to image “S”, which haspreviously been registered to image “G”. Thus, the signal from image “N”is indirectly registered to image “G”.

As used herein, the term “fiducial” is intended to mean adistinguishable point of reference in or on an object. The point ofreference can be, for example, a mark, second object, shape, edge, area,irregularity, channel, pit, post or the like. The point of reference canbe present in an image of the object or in another data set derived fromdetecting the object. The point of reference can be specified by an xand/or y coordinate in a plane of the object. Alternatively oradditionally, the point of reference can be specified by a z coordinatethat is orthogonal to the xy plane, for example, being defined by therelative locations of the object and a detector. One or more coordinatesfor a point of reference can be specified relative to one or more otheranalytes of an object or of an image or other data set derived from theobject.

As used herein, the term “optical signal” is intended to include, forexample, fluorescent, luminescent, scatter, or absorption signals.Optical signals can be detected in the ultraviolet (UV) range (about 200to 390 nm), visible (VIS) range (about 391 to 770 nm), infrared (IR)range (about 0.771 to 25 microns), or other range of the electromagneticspectrum. Optical signals can be detected in a way that excludes all orpart of one or more of these ranges.

As used herein, the term “signal level” is intended to mean an amount orquantity of detected energy or coded information that has a desired orpredefined characteristic. For example, an optical signal can bequantified by one or more of intensity, wavelength, energy, frequency,power, luminance or the like. Other signals can be quantified accordingto characteristics such as voltage, current, electric field strength,magnetic field strength, frequency, power, temperature, etc. Absence ofsignal is understood to be a signal level of zero or a signal level thatis not meaningfully distinguished from noise.

As used herein, the term “simulate” is intended to mean creating arepresentation or model of a physical thing or action that predictscharacteristics of the thing or action. The representation or model canin many cases be distinguishable from the thing or action. For example,the representation or model can be distinguishable from a thing withrespect to one or more characteristic such as color, intensity ofsignals detected from all or part of the thing, size, or shape. Inparticular implementations, the representation or model can beidealized, exaggerated, muted, or incomplete when compared to the thingor action. Thus, in some implementations, a representation of model canbe distinguishable from the thing or action that it represents, forexample, with respect to at least one of the characteristics set forthabove. The representation or model can be provided in a computerreadable format or medium such as one or more of those set forthelsewhere herein.

As used herein, the term “specific signal” is intended to mean detectedenergy or coded information that is selectively observed over otherenergy or information such as background energy or information. Forexample, a specific signal can be an optical signal detected at aparticular intensity, wavelength or color; an electrical signal detectedat a particular frequency, power or field strength; or other signalsknown in the art pertaining to spectroscopy and analytical detection.

As used herein, the term “swath” is intended to mean a rectangularportion of an object. The swath can be an elongated strip that isscanned by relative movement between the object and a detector in adirection that is parallel to the longest dimension of the strip.Generally, the width of the rectangular portion or strip will beconstant along its full length. Multiple swaths of an object can beparallel to each other. Multiple swaths of an object can be adjacent toeach other, overlapping with each other, abutting each other, orseparated from each other by an interstitial area.

As used herein, the term “variance” is intended to mean a differencebetween that which is expected and that which is observed or adifference between two or more observations. For example, variance canbe the discrepancy between an expected value and a measured value.Variance can be represented using statistical functions such as standarddeviation, the square of standard deviation, coefficient of variation orthe like.

As used herein, the term “xy coordinates” is intended to meaninformation that specifies location, size, shape, and/or orientation inan xy plane. The information can be, for example, numerical coordinatesin a Cartesian system. The coordinates can be provided relative to oneor both of the x and y axes or can be provided relative to anotherlocation in the xy plane. For example, coordinates of a analyte of anobject can specify the location of the analyte relative to location of afiducial or other analyte of the object.

As used herein, the term “xy plane” is intended to mean a 2 dimensionalarea defined by straight line axes x and y. When used in reference to adetector and an object observed by the detector, the area can be furtherspecified as being orthogonal to the direction of observation betweenthe detector and object being detected.

As used herein, the term “z coordinate” is intended to mean informationthat specifies the location of a point, line or area along an axes thatis orthogonal to an xy plane. In particular implementations, the z axisis orthogonal to an area of an object that is observed by a detector.For example, the direction of focus for an optical system may bespecified along the z axis.

In some implementations, acquired signal data is transformed using anaffine transformation. In some such implementations, template generationmakes use of the fact that the affine transforms between color channelsare consistent between runs. Because of this consistency, a set ofdefault offsets can be used when determining the coordinates of theanalytes in a specimen. For example, a default offsets file can containthe relative transformation (shift, scale, skew) for the differentchannels relative to one channel, such as the A channel. In otherimplementations, however, the offsets between color channels driftduring a run and/or between runs, making offset-driven templategeneration difficult. In such implementations, the methods and systemsprovided herein can utilize offset-less template generation, which isdescribed further below.

In some aspects of the above implementations, the system can comprise aflow cell. In some aspects, the flow cell comprises lanes, or otherconfigurations, of tiles, wherein at least some of the tiles compriseone or more arrays of analytes. In some aspects, the analytes comprise aplurality of molecules such as nucleic acids. In certain aspects, theflow cell is configured to deliver a labeled nucleotide base to an arrayof nucleic acids, thereby extending a primer hybridized to a nucleicacid within a analyte so as to produce a signal corresponding to aanalyte comprising the nucleic acid. In preferred implementations, thenucleic acids within a analyte are identical or substantially identicalto each other.

In some of the systems for image analysis described herein, each imagein the set of images includes color signals, wherein a different colorcorresponds to a different nucleotide base. In some aspects, each imageof the set of images comprises signals having a single color selectedfrom at least four different colors. In some aspects, each image in theset of images comprises signals having a single color selected from fourdifferent colors. In some of the systems described herein, nucleic acidscan be sequenced by providing four different labeled nucleotide bases tothe array of molecules so as to produce four different images, eachimage comprising signals having a single color, wherein the signal coloris different for each of the four different images, thereby producing acycle of four color images that corresponds to the four possiblenucleotides present at a particular position in the nucleic acid. Incertain aspects, the system comprises a flow cell that is configured todeliver additional labeled nucleotide bases to the array of molecules,thereby producing a plurality of cycles of color images.

In preferred implementations, the methods provided herein can includedetermining whether a processor is actively acquiring data or whetherthe processor is in a low activity state. Acquiring and storing largenumbers of high-quality images typically requires massive amounts ofstorage capacity. Additionally, once acquired and stored, the analysisof image data can become resource intensive and can interfere withprocessing capacity of other functions, such as ongoing acquisition andstorage of additional image data. Accordingly, as used herein, the termlow activity state refers to the processing capacity of a processor at agiven time. In some implementations, a low activity state occurs when aprocessor is not acquiring and/or storing data. In some implementations,a low activity state occurs when some data acquisition and/or storage istaking place, but additional processing capacity remains such that imageanalysis can occur at the same time without interfering with otherfunctions.

As used herein, “identifying a conflict” refers to identifying asituation where multiple processes compete for resources. In some suchimplementations, one process is given priority over another process. Insome implementations, a conflict may relate to the need to give priorityfor allocation of time, processing capacity, storage capacity or anyother resource for which priority is given. Thus, in someimplementations, where processing time or capacity is to be distributedbetween two processes such as either analyzing a data set and acquiringand/or storing the data set, a conflict between the two processes existsand can be resolved by giving priority to one of the processes.

Also provided herein are systems for performing image analysis. Thesystems can include a processor; a storage capacity; and a program forimage analysis, the program comprising instructions for processing afirst data set for storage and the second data set for analysis, whereinthe processing comprises acquiring and/or storing the first data set onthe storage device and analyzing the second data set when the processoris not acquiring the first data set. In certain aspects, the programincludes instructions for identifying at least one instance of aconflict between acquiring and/or storing the first data set andanalyzing the second data set; and resolving the conflict in favor ofacquiring and/or storing image data such that acquiring and/or storingthe first data set is given priority. In certain aspects, the first dataset comprises image files obtained from an optical imaging device. Incertain aspects, the system further comprises an optical imaging device.In some aspects, the optical imaging device comprises a light source anda detection device.

As used herein, the term “program” refers to instructions or commands toperform a task or process. The term “program” can be usedinterchangeably with the term module. In certain implementations, aprogram can be a compilation of various instructions executed under thesame set of commands In other implementations, a program can refer to adiscrete batch or file.

Set forth below are some of the surprising effects of utilizing themethods and systems for performing image analysis set forth herein. Insome sequencing implementations, an important measure of a sequencingsystem's utility is its overall efficiency. For example, the amount ofmappable data produced per day and the total cost of installing andrunning the instrument are important aspects of an economical sequencingsolution. To reduce the time to generate mappable data and to increasethe efficiency of the system, real-time base calling can be enabled onan instrument computer and can run in parallel with sequencing chemistryand imaging. This allows much of the data processing and analysis to becompleted before the sequencing chemistry finishes. Additionally, it canreduce the storage required for intermediate data and limit the amountof data that needs to travel across the network.

While sequence output has increased, the data per run transferred fromthe systems provided herein to the network and to secondary analysisprocessing hardware has substantially decreased. By transforming data onthe instrument computer (acquiring computer), network loads aredramatically reduced. Without these on-instrument, off-network datareduction techniques, the image output of a fleet of DNA sequencinginstruments would cripple most networks.

The widespread adoption of the high-throughput DNA sequencinginstruments has been driven in part by ease of use, support for a rangeof applications, and suitability for virtually any lab environment. Thehighly efficient algorithms presented herein allow significant analysisfunctionality to be added to a simple workstation that can controlsequencing instruments. This reduction in the requirements forcomputational hardware has several practical benefits that will becomeeven more important as sequencing output levels continue to increase.For example, by performing image analysis and base calling on a simpletower, heat production, laboratory footprint, and power consumption arekept to a minimum In contrast, other commercial sequencing technologieshave recently ramped up their computing infrastructure for primaryanalysis, with up to five times more processing power, leading tocommensurate increases in heat output and power consumption. Thus, insome implementations, the computational efficiency of the methods andsystems provided herein enables customers to increase their sequencingthroughput while keeping server hardware expenses to a minimum.

Accordingly, in some implementations, the methods and/or systemspresented herein act as a state machine, keeping track of the individualstate of each specimen, and when it detects that a specimen is ready toadvance to the next state, it does the appropriate processing andadvances the specimen to that state. A more detailed example of how thestate machine monitors a file system to determine when a specimen isready to advance to the next state according to a preferredimplementation is set forth in Example 1 below.

In preferred implementations, the methods and systems provided hereinare multi-threaded and can work with a configurable number of threads.Thus, for example in the context of nucleic acid sequencing, the methodsand systems provided herein are capable of working in the backgroundduring a live sequencing run for real-time analysis, or it can be runusing a pre-existing set of image data for off-line analysis. In certainpreferred implementations, the methods and systems handlemulti-threading by giving each thread its own subset of specimen forwhich it is responsible. This minimizes the possibility of threadcontention.

A method of the present disclosure can include a step of obtaining atarget image of an object using a detection apparatus, wherein the imageincludes a repeating pattern of analytes on the object. Detectionapparatus that are capable of high resolution imaging of surfaces areparticularly useful. In particular implementations, the detectionapparatus will have sufficient resolution to distinguish analytes at thedensities, pitches, and/or analyte sizes set forth herein. Particularlyuseful are detection apparatus capable of obtaining images or image datafrom surfaces. Example detectors are those that are configured tomaintain an object and detector in a static relationship while obtainingan area image. Scanning apparatus can also be used. For example, anapparatus that obtains sequential area images (e.g., so called ‘step andshoot’ detectors) can be used. Also useful are devices that continuallyscan a point or line over the surface of an object to accumulate data toconstruct an image of the surface. Point scanning detectors can beconfigured to scan a point (i.e., a small detection area) over thesurface of an object via a raster motion in the x-y plane of thesurface. Line scanning detectors can be configured to scan a line alongthey dimension of the surface of an object, the longest dimension of theline occurring along the x dimension. It will be understood that thedetection device, object or both can be moved to achieve scanningdetection. Detection apparatus that are particularly useful, for examplein nucleic acid sequencing applications, are described in US Pat App.Pub. Nos. 2012/0270305 A1; 2013/0023422 A1; and 2013/0260372 A1; andU.S. Pat. Nos. 5,528,050; 5,719,391; 8,158,926 and 8,241,573, each ofwhich is incorporated herein by reference.

The implementations disclosed herein may be implemented as a method,apparatus, system or article of manufacture using programming orengineering techniques to produce software, firmware, hardware, or anycombination thereof. The term “article of manufacture” as used hereinrefers to code or logic implemented in hardware or computer readablemedia such as optical storage devices, and volatile or non-volatilememory devices. Such hardware may include, but is not limited to, fieldprogrammable gate arrays (FPGAs), coarse grained reconfigurablearchitectures (CGRAs), application-specific integrated circuits (ASICs),complex programmable logic devices (CPLDs), programmable logic arrays(PLAs), microprocessors, or other similar processing devices. Inparticular implementations, information or algorithms set forth hereinare present in non-transient storage media.

In particular implementations, a computer implemented method set forthherein can occur in real time while multiple images of an object arebeing obtained. Such real time analysis is particularly useful fornucleic acid sequencing applications wherein an array of nucleic acidsis subjected to repeated cycles of fluidic and detection steps. Analysisof the sequencing data can often be computationally intensive such thatit can be beneficial to perform the methods set forth herein in realtime or in the background while other data acquisition or analysisalgorithms are in process. Example real time analysis methods that canbe used with the present methods are those used for the MiSeq and HiSeqsequencing devices commercially available from Illumina, Inc. (SanDiego, Calif.) and/or described in US Pat. App. Pub. No. 2012/0020537A1, which is incorporated herein by reference.

An example data analysis system, formed by one or more programmedcomputers, with programming being stored on one or more machine readablemedia with code executed to carry out one or more steps of methodsdescribed herein. In one implementation, for example, the systemincludes an interface designed to permit networking of the system to oneor more detection systems (e.g., optical imaging systems) that areconfigured to acquire data from target objects. The interface mayreceive and condition data, where appropriate. In particularimplementations the detection system will output digital image data, forexample, image data that is representative of individual pictureelements or pixels that, together, form an image of an array or otherobject. A processor processes the received detection data in accordancewith a one or more routines defined by processing code. The processingcode may be stored in various types of memory circuitry.

In accordance with the presently contemplated implementations, theprocessing code executed on the detection data includes a data analysisroutine designed to analyze the detection data to determine thelocations and metadata of individual analytes visible or encoded in thedata, as well as locations at which no analyte is detected (i.e., wherethere is no analyte, or where no meaningful signal was detected from anexisting analyte). In particular implementations, analyte locations inan array will typically appear brighter than non-analyte locations dueto the presence of fluorescing dyes attached to the imaged analytes. Itwill be understood that the analytes need not appear brighter than theirsurrounding area, for example, when a target for the probe at theanalyte is not present in an array being detected. The color at whichindividual analytes appear may be a function of the dye employed as wellas of the wavelength of the light used by the imaging system for imagingpurposes. Analytes to which targets are not bound or that are otherwisedevoid of a particular label can be identified according to othercharacteristics, such as their expected location in the microarray.

Once the data analysis routine has located individual analytes in thedata, a value assignment may be carried out. In general, the valueassignment will assign a digital value to each analyte based uponcharacteristics of the data represented by detector components (e.g.,pixels) at the corresponding location. That is, for example when imagingdata is processed, the value assignment routine may be designed torecognize that a specific color or wavelength of light was detected at aspecific location, as indicated by a group or cluster of pixels at thelocation. In a typical DNA imaging application, for example, the fourcommon nucleotides will be represented by four separate anddistinguishable colors. Each color, then, may be assigned a valuecorresponding to that nucleotide.

As used herein, the terms “module”, “system,” or “system controller” mayinclude a hardware and/or software system and circuitry that operates toperform one or more functions. For example, a module, system, or systemcontroller may include a computer processor, controller, or otherlogic-based device that performs operations based on instructions storedon a tangible and non-transitory computer readable storage medium, suchas a computer memory. Alternatively, a module, system, or systemcontroller may include a hard-wired device that performs operationsbased on hard-wired logic and circuitry. The module, system, or systemcontroller shown in the attached figures may represent the hardware andcircuitry that operates based on software or hardwired instructions, thesoftware that directs hardware to perform the operations, or acombination thereof. The module, system, or system controller caninclude or represent hardware circuits or circuitry that include and/orare connected with one or more processors, such as one or computermicroprocessors.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by acomputer, including RAM memory, ROM memory, EPROM memory, EEPROM memory,and non-volatile RAM (NVRAM) memory. The above memory types are examplesonly, and are thus not limiting as to the types of memory usable forstorage of a computer program.

In the molecular biology field, one of the processes for nucleic acidsequencing in use is sequencing-by-synthesis. The technique can beapplied to massively parallel sequencing projects. For example, by usingan automated platform, it is possible to carry out hundreds of thousandsof sequencing reactions simultaneously. Thus, one of the implementationsof the present invention relates to instruments and methods foracquiring, storing, and analyzing image data generated during nucleicacid sequencing.

Enormous gains in the amount of data that can be acquired and storedmake streamlined image analysis methods even more beneficial. Forexample, the image analysis methods described herein permit bothdesigners and end users to make efficient use of existing computerhardware. Accordingly, presented herein are methods and systems whichreduce the computational burden of processing data in the face ofrapidly increasing data output. For example, in the field of DNAsequencing, yields have scaled 15-fold over the course of a recent year,and can now reach hundreds of gigabases in a single run of a DNAsequencing device. If computational infrastructure requirements grewproportionately, large genome-scale experiments would remain out ofreach to most researchers. Thus, the generation of more raw sequencedata will increase the need for secondary analysis and data storage,making optimization of data transport and storage extremely valuable.Some implementations of the methods and systems presented herein canreduce the time, hardware, networking, and laboratory infrastructurerequirements needed to produce usable sequence data.

The present disclosure describes various methods and systems forcarrying out the methods. Examples of some of the methods are describedas a series of steps. However, it should be understood thatimplementations are not limited to the particular steps and/or order ofsteps described herein. Steps may be omitted, steps may be modified,and/or other steps may be added. Moreover, steps described herein may becombined, steps may be performed simultaneously, steps may be performedconcurrently, steps may be split into multiple sub-steps, steps may beperformed in a different order, or steps (or a series of steps) may bere-performed in an iterative fashion. In addition, although differentmethods are set forth herein, it should be understood that the differentmethods (or steps of the different methods) may be combined in otherimplementations.

In some implementations, a processing unit, processor, module, orcomputing system that is “configured to” perform a task or operation maybe understood as being particularly structured to perform the task oroperation (e.g., having one or more programs or instructions storedthereon or used in conjunction therewith tailored or intended to performthe task or operation, and/or having an arrangement of processingcircuitry tailored or intended to perform the task or operation). Forthe purposes of clarity and the avoidance of doubt, a general purposecomputer (which may become “configured to” perform the task or operationif appropriately programmed) is not “configured to” perform a task oroperation unless or until specifically programmed or structurallymodified to perform the task or operation.

Moreover, the operations of the methods described herein can besufficiently complex such that the operations cannot be mentallyperformed by an average human being or a person of ordinary skill in theart within a commercially reasonable time period. For example, themethods may rely on relatively complex computations such that such aperson cannot complete the methods within a commercially reasonabletime.

Throughout this application various publications, patents or patentapplications have been referenced. The disclosures of these publicationsin their entireties are hereby incorporated by reference in thisapplication in order to more fully describe the state of the art towhich this invention pertains.

The term “comprising” is intended herein to be open-ended, including notonly the recited elements, but further encompassing any additionalelements.

As used herein, the term “each”, when used in reference to a collectionof items, is intended to identify an individual item in the collectionbut does not necessarily refer to every item in the collection.Exceptions can occur if explicit disclosure or context clearly dictatesotherwise.

Although the invention has been described with reference to the examplesprovided above, it should be understood that various modifications canbe made without departing from the invention.

The modules in this application can be implemented in hardware orsoftware, and need not be divided up in precisely the same blocks asshown in the figures. Some can also be implemented on differentprocessors or computers, or spread among a number of differentprocessors or computers. In addition, it will be appreciated that someof the modules can be combined, operated in parallel or in a differentsequence than that shown in the figures without affecting the functionsachieved. Also as used herein, the term “module” can include“sub-modules”, which themselves can be considered herein to constitutemodules. The blocks in the figures designated as modules can also bethought of as flowchart steps in a method.

As used herein, the “identification” of an item of information does notnecessarily require the direct specification of that item ofinformation. Information can be “identified” in a field by simplyreferring to the actual information through one or more layers ofindirection, or by identifying one or more items of differentinformation which are together sufficient to determine the actual itemof information. In addition, the term “specify” is used herein to meanthe same as “identify”.

As used herein, a given signal, event or value is “in dependence upon” apredecessor signal, event or value of the predecessor signal, event orvalue influenced by the given signal, event or value. If there is anintervening processing element, step or time period, the given signal,event or value can still be “in dependence upon” the predecessor signal,event or value. If the intervening processing element or step combinesmore than one signal, event or value, the signal output of theprocessing element or step is considered “in dependence upon” each ofthe signal, event or value inputs. If the given signal, event or valueis the same as the predecessor signal, event or value, this is merely adegenerate case in which the given signal, event or value is stillconsidered to be “in dependence upon” or “dependent on” or “based on”the predecessor signal, event or value. “Responsiveness” of a givensignal, event or value upon another signal, event or value is definedsimilarly.

As used herein, “concurrently” or “in parallel” does not require exactsimultaneity. It is sufficient if the evaluation of one of theindividuals begins before the evaluation of another of the individualscompletes.

Computer System

FIG. 82 is a computer system 8200 that can be used by the sequencingsystem 800A to implement the technology disclosed herein. Computersystem 8200 includes at least one central processing unit (CPU) 8272that communicates with a number of peripheral devices via bus subsystem8255. These peripheral devices can include a storage subsystem 8210including, for example, memory devices and a file storage subsystem8236, user interface input devices 8238, user interface output devices8276, and a network interface subsystem 8274. The input and outputdevices allow user interaction with computer system 8200. Networkinterface subsystem 8274 provides an interface to outside networks,including an interface to corresponding interface devices in othercomputer systems.

In one implementation, the system controller 7806 is communicably linkedto the storage subsystem 8210 and the user interface input devices 8238.

User interface input devices 8238 can include a keyboard; pointingdevices such as a mouse, trackball, touchpad, or graphics tablet; ascanner; a touch screen incorporated into the display; audio inputdevices such as voice recognition systems and microphones; and othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computer system 8200.

User interface output devices 8276 can include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem can include an LED display, a cathode raytube (CRT), a flat-panel device such as a liquid crystal display (LCD),a projection device, or some other mechanism for creating a visibleimage. The display subsystem can also provide a non-visual display suchas audio output devices. In general, use of the term “output device” isintended to include all possible types of devices and ways to outputinformation from computer system 8200 to the user or to another machineor computer system.

Storage subsystem 8210 stores programming and data constructs thatprovide the functionality of some or all of the modules and methodsdescribed herein. These software modules are generally executed by deeplearning processors 8278.

Deep learning processors 8278 can be graphics processing units (GPUs),field-programmable gate arrays (FPGAs), application-specific integratedcircuits (ASICs), and/or coarse-grained reconfigurable architectures(CGRAs). Deep learning processors 8278 can be hosted by a deep learningcloud platform such as Google Cloud Platform™, Xilinx™, and Cirrascale™.Examples of deep learning processors 8278 include Google's TensorProcessing Unit (TPU)™, rackmount solutions like GX4 Rackmount Series™,GX82 Rackmount Series™, NVIDIA DGX-1™ Microsoft′ Stratix V FPGA™,Graphcore's Intelligent Processor Unit (IPU)™, Qualcomm's ZerothPlatform™ with Snapdragon Processors™, NVIDIA's Volta™, NVIDIA's DRIVEPX™, NVIDIA's JETSON TX1/TX2 MODULE™, Intel's Nirvana™, Movidius VPU™,Fujitsu DPI™ ARM's DynamicIQ™, IBM TrueNorth™, Lambda GPU Server withTestaV100s™, and others.

Memory subsystem 8222 used in the storage subsystem 8210 can include anumber of memories including a main random access memory (RAM) 8232 forstorage of instructions and data during program execution and a readonly memory (ROM) 8234 in which fixed instructions are stored. A filestorage subsystem 8236 can provide persistent storage for program anddata files, and can include a hard disk drive, a floppy disk drive alongwith associated removable media, a CD-ROM drive, an optical drive, orremovable media cartridges. The modules implementing the functionalityof certain implementations can be stored by file storage subsystem 8236in the storage subsystem 8210, or in other machines accessible by theprocessor.

Bus subsystem 8255 provides a mechanism for letting the variouscomponents and subsystems of computer system 8200 communicate with eachother as intended. Although bus subsystem 8255 is shown schematically asa single bus, alternative implementations of the bus subsystem can usemultiple busses.

Computer system 8200 itself can be of varying types including a personalcomputer, a portable computer, a workstation, a computer terminal, anetwork computer, a television, a mainframe, a server farm, awidely-distributed set of loosely networked computers, or any other dataprocessing system or user device. Due to the ever-changing nature ofcomputers and networks, the description of computer system 8200 depictedin FIG. 82 is intended only as a specific example for purposes ofillustrating the preferred implementations of the present invention.Many other configurations of computer system 8200 are possible havingmore or less components than the computer system depicted in FIG. 82.

Particular Implementations

We describe various implementations of neural network-based templategeneration and neural network-based base calling. One or more featuresof an implementation can be combined with the base implementation.Implementations that are not mutually exclusive are taught to becombinable. One or more features of an implementation can be combinedwith other implementations. This disclosure periodically reminds theuser of these options. Omission from some implementations of recitationsthat repeat these options should not be taken as limiting thecombinations taught in the preceding sections—these recitations arehereby incorporated forward by reference into each of the followingimplementations.

Subpixel Base Calling

We disclose a computer-implemented method of determining metadata aboutanalytes on a tile of a flow cell. The method includes accessing aseries of image sets generated during a sequencing run, each image setin the series generated during a respective sequencing cycle of thesequencing run, each image in the series depicting the analytes andtheir surrounding background, and each image in the series having aplurality of subpixels. The method includes obtaining, from a basecaller, a base call classifying each of the subpixels as one of fourbases (A, C, T, and G), thereby producing a base call sequence for eachof the subpixels across a plurality of sequencing cycles of thesequencing run. The method includes generating an analyte map thatidentifies the analytes as disjointed regions of contiguous subpixelswhich share a substantially matching base call sequence. The methodincludes determining spatial distribution of analytes, including theirshapes and sizes based on the disjointed regions and storing the analytemap in memory for use as ground truth for training a classifier.

The method described in this section and other sections of thetechnology disclosed can include one or more of the following featuresand/or features described in connection with additional methodsdisclosed. In the interest of conciseness, the combinations of featuresdisclosed in this application are not individually enumerated and arenot repeated with each base set of features. The reader will understandhow features identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the method includes identifying as backgroundthose subpixels in the analyte map that do not belong to any of thedisjointed regions. In one implementation, the method includesobtaining, from the base caller, the base call classifying each of thesubpixels as one of five bases (A, C, T, G, and N). In oneimplementation, the analyte map identifies analyte boundary portionsbetween two contiguous subpixels whose base call sequences do notsubstantially match.

In one implementation, the method includes identifying origin subpixelsat preliminary center coordinates of the analytes determined by the basecaller, and breadth-first searching for substantially matching base callsequences by beginning with the origin subpixels and continuing withsuccessively contiguous non-origin subpixels. In one implementation, themethod includes, on an analyte-by-analyte basis, determininghyperlocated center coordinates of the analytes by calculating centersof mass of the disjointed regions of the analyte map as an average ofcoordinates of respective contiguous subpixels forming the disjointedregions, and storing the hyperlocated center coordinates of the analytesin the memory on the analyte-by-analyte basis for use as ground truthfor training the classifier.

In one implementation, the method includes, on the analyte-by-analytebasis, identifying centers of mass subpixels in the disjointed regionsof the analyte map at the hyperlocated center coordinates of theanalytes, upsampling the analyte map using interpolation and storing theupsampled analyte map in the memory for use as ground truth for trainingthe classifier, and, in the upsampled analyte map, on theanalyte-by-analyte basis, assigning a value to each contiguous subpixelin the disjointed regions based on a decay factor that is proportionalto distance of a contiguous subpixel from a center of mass subpixel in adisjointed region to which the contiguous subpixel belongs. In oneimplementation, the value is a intensity value normalized between zeroand one. In one implementation, the method includes, in the upsampledanalyte map, assigning a same predetermined value to all the subpixelsidentified as the background. In one implementation, the predeterminedvalue is a zero intensity value.

In one implementation, the method includes generating a decay map fromthe upsampled analyte map that expresses the contiguous subpixels in thedisjointed regions and the subpixels identified as the background basedon their assigned values, and storing the decay map in the memory foruse as ground truth for training the classifier. In one implementation,each subpixel in the decay map has a value normalized between zero andone. In one implementation, the method includes, in the upsampledanalyte map, categorizing, on the analyte-by-analyte basis, thecontiguous subpixels in the disjointed regions as analyte interiorsubpixels belonging to a same analyte, the centers of mass subpixels asanalyte center subpixels, subpixels containing the analyte boundaryportions as boundary subpixels, and the subpixels identified as thebackground as background subpixels, and storing the categorizations inthe memory for use as ground truth for training the classifier.

In one implementation, the method includes, storing, on theanalyte-by-analyte basis, coordinates of the analyte interior subpixels,the analyte center subpixels, the boundary subpixels, and the backgroundsubpixels in the memory for use as ground truth for training theclassifier, downscaling the coordinates by a factor used to upsample theanalyte map, and, storing, on the analyte-by-analyte basis, thedownscaled coordinates in the memory for use as ground truth fortraining the classifier.

In one implementation, the method includes, in a binary ground truthdata generated from the upsampled analyte map, using color coding tolabel the analyte center subpixels as belonging to an analyte centerclass and all other subpixels are belonging to a non-center class, andstoring the binary ground truth data in the memory for use as groundtruth for training the classifier. In one implementation, the methodincludes, in a ternary ground truth data generated from the upsampledanalyte map, using color coding to label the background subpixels asbelonging to a background class, the analyte center subpixels asbelonging to an analyte center class, and the analyte interior subpixelsas belonging to an analyte interior class, and storing the ternaryground truth data in the memory for use as ground truth for training theclassifier.

In one implementation, the method includes generating analyte maps for aplurality of tiles of the flow cell, storing the analyte maps in memoryand determining spatial distribution of analytes in the tiles based onthe analyte maps, including their shapes and sizes, in the upsampledanalyte maps of the analytes in the tiles, categorizing, on ananalyte-by-analyte basis, subpixels as analyte interior subpixelsbelonging to a same analyte, analyte center subpixels, boundarysubpixels, and background subpixels, storing the categorizations in thememory for use as ground truth for training the classifier, storing, onthe analyte-by-analyte basis across the tiles, coordinates of theanalyte interior subpixels, the analyte center subpixels, the boundarysubpixels, and the background subpixels in the memory for use as groundtruth for training the classifier, downscaling the coordinates by thefactor used to upsample the analyte map, and, storing, on theanalyte-by-analyte basis across the tiles, the downscaled coordinates inthe memory for use as ground truth for training the classifier.

In one implementation, the base call sequences are substantiallymatching when a predetermined portion of base calls match on an ordinalposition-wise basis. In one implementation, the base caller produces thebase call sequences by interpolating intensity of the subpixels,including at least one of nearest neighbor intensity extraction,Gaussian based intensity extraction, intensity extraction based onaverage of 2×2 subpixel area, intensity extraction based on brightest of2×2 subpixel area, intensity extraction based on average of 3×3 subpixelarea, bilinear intensity extraction, bicubic intensity extraction,and/or intensity extraction based on weighted area coverage. In oneimplementation, the subpixels are identified to the base caller based ontheir integer or non-integer coordinates.

In one implementation, the method includes requiring that at least someof the disjointed regions have a predetermined minimum number ofsubpixels. In one implementation, the flow cell has at least onepatterned surface with an array of wells that occupy the analytes. Insuch an implementation, the method includes, based on the determinedshapes and sizes of the analytes, determining which ones of the wellsare substantially occupied by at least one analyte, which ones of thewells are minimally occupied, and which ones of the wells areco-occupied by multiple analytes.

In one implementation, the flow cell has at least one nonpatternedsurface and the analytes are unevenly scattered over the nonpatternedsurface. In one implementation, the density of the analytes ranges fromabout 100,000 analytes/mm² to about 1,000,000 analytes/mm². In oneimplementation, the density of the analytes ranges from about 1,000,000analytes/mm² to about 10,000,000 analytes/mm². In one implementation,the subpixels are quarter subpixels. In another implementation, thesubpixels are half subpixels. In one implementation, the preliminarycenter coordinates of the analytes determined by the base caller aredefined in a template image of the tile, and a pixel resolution, animage coordinate system, and measurement scales of the image coordinatesystem are same for the template image and the images. In oneimplementation, each image set has four images. In anotherimplementation, each image set has two images. In yet anotherimplementation, each image set has one image. In one implementation, thesequencing run utilizes four-channel chemistry. In anotherimplementation, the sequencing run utilizes two-channel chemistry. Inyet another implementation, the sequencing run utilizes one-channelchemistry.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

We disclose a computer-implemented method of determining metadata aboutanalytes on a tile of a flow cell. The method includes accessing a setof images of the tile captured during a sequencing run and preliminarycenter coordinates of the analytes determined by a base caller. Themethod includes, for each image set, obtaining, from a base caller, abase call classifying, as one of four bases origin subpixels thatcontain the preliminary center coordinates and a predeterminedneighborhood of contiguous subpixels that are successively contiguous torespective ones of the origin subpixels, thereby producing a base callsequence for each of the origin subpixels and for each of thepredetermined neighborhood of contiguous subpixels. The method includesgenerating an analyte map that identifies the analytes as disjointedregions of contiguous subpixels that are successively contiguous to atleast some of the respective ones of the origin subpixels and share asubstantially matching base call sequence of the one of four bases withthe at least some of the respective ones of the origin subpixels. Themethod includes storing the analyte map in memory and determining theshapes and the sizes of the analytes based on the disjointed regions inthe analyte map.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the predetermined neighborhood of contiguoussubpixels is a m×n subpixel patch centered at pixels containing theorigin subpixels and the subpixel patch is 3×3 pixels. In oneimplementation, the predetermined neighborhood of contiguous subpixelsis a n-connected subpixel neighborhood centered at pixels containing theorigin subpixels. In one implementation, the method includes,identifying as background those subpixels in the analyte map that do notbelong to any of the disjointed regions.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Training Data Generation

We disclose a computer-implemented method of generating training datafor neural network-based template generation and base calling. Themethod includes accessing a multitude of images of a flow cell capturedover a plurality of cycles of a sequencing run, the flow cell having aplurality of tiles and, in the multitude of images, each of the tileshaving a sequence of image sets generated over the plurality of cycles,and each image in the sequence of image sets depicting intensityemissions of analytes and their surrounding background on a particularone of the tiles at a particular one the cycles. The method includesconstructing a training set having a plurality of training examples,each training example corresponding to a particular one of the tiles andincluding image data from at least some image sets in the sequence ofimage sets of the particular one of the tiles. The method includesgenerating at least one ground truth data representation for each of thetraining examples, the ground truth data representation identifying atleast one of spatial distribution of analytes and their surroundingbackground on the particular one of the tiles whose intensity emissionsare depicted by the image data, including at least one of analyteshapes, analyte sizes, and/or analyte boundaries, and/or centers of theanalytes.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the image data includes images in each of the atleast some image sets in the sequence of image sets of the particularone of the tiles, and the images have a resolution of 1800×1800. In oneimplementation, the image data includes at least one image patch fromeach of the images, and the image patch covers a portion of theparticular one of the tiles and has a resolution of 20×20. In oneimplementation, the image data includes an upsampled representation ofthe image patch, and the upsampled representation has a resolution of80×80. In one implementation, the ground truth data representation hasan upsampled resolution of 80×80.

In one implementation, multiple training examples correspond to a sameparticular one of the tiles and respectively include as image datadifferent image patches from each image in each of at least some imagesets in a sequence of image sets of the same particular one of thetiles, and at least some of the different image patches overlap witheach other. In one implementation, the ground truth data representationidentifies the analytes as disjoint regions of adjoining subpixels, thecenters of the analytes as centers of mass subpixels within respectiveones of the disjoint regions, and their surrounding background assubpixels that do not belong to any of the disjoint regions. In oneimplementation, the ground truth data representation uses color codingto identify each subpixel as either being a analyte center or anon-center. In one implementation, the ground truth data representationuses color coding to identify each subpixel as either being analyteinterior, analyte center, or surrounding background.

In one implementation, the method includes, storing, in memory, thetraining examples in the training set and associated ground truth datarepresentations as the training data for the neural network-basedtemplate generation and base calling. In one implementation, the methodincludes generating the training data for a variety of flow cells,sequencing instruments, sequencing protocols, sequencing chemistries,sequencing reagents, and analyte densities.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Metadata & Base Calls Generation

In one implementation, a method includes accessing sequencing images ofanalytes produced by a sequencer, generating training data from thesequencing images, and using the training data for training a neuralnetwork to generate metadata about the analytes. Each of the featuresdiscussed in the particular implementation section for otherimplementations apply equally to this implementation. As indicatedabove, all the other features are not repeated here and should beconsidered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations. Otherimplementations of the method described in this section can include anon-transitory computer readable storage medium storing instructionsexecutable by a processor to perform any of the methods described above.Yet another implementation of the method described in this section caninclude a system including memory and one or more processors operable toexecute instructions, stored in the memory, to perform any of themethods described above.

In one implementation, a method includes accessing sequencing images ofanalytes produced by a sequencer, generating training data from thesequencing images, and using the training data for training a neuralnetwork to base call the analytes. Each of the features discussed in theparticular implementation section for other implementations applyequally to this implementation. As indicated above, all the otherfeatures are not repeated here and should be considered repeated byreference. The reader will understand how features identified in theseimplementations can readily be combined with sets of base featuresidentified in other implementations. Other implementations of the methoddescribed in this section can include a non-transitory computer readablestorage medium storing instructions executable by a processor to performany of the methods described above. Yet another implementation of themethod described in this section can include a system including memoryand one or more processors operable to execute instructions, stored inthe memory, to perform any of the methods described above.

Regression Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the inputimage data. Each image in the sequence of image sets covers the tile,and depicts intensity emissions of analytes on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. The method includes processing thealternative representation through an output layer and generating anoutput that identifies analytes, whose intensity emissions are depictedby the input image data, as disjoint regions of adjoining subpixels,centers of the analytes as center subpixels at centers of mass of therespective ones of the disjoint regions, and their surroundingbackground as background subpixels not belonging to any of the disjointregions.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the adjoining subpixels in the respective ones ofthe disjoint regions have intensity values weighted according todistance of an adjoining subpixel from a center subpixel in a disjointregion to which the adjoining subpixel belongs. In one implementation,the center subpixels have highest intensity values within the respectiveones of the disjoint regions. In one implementation, the backgroundsubpixels all have a same lowest intensity value in the output. In oneimplementation, the output layer normalizes the intensity values betweenzero and one.

In one implementation, the method includes applying a peak locator tothe output to find peak intensities in the output, determining locationcoordinates of the centers of the analytes based on the peakintensities, downscaling the location coordinates by an upsamplingfactor used to prepare the input image data, and storing the downscaledlocation coordinates in memory for use in base calling the analytes. Inone implementation, the method includes categorizing the adjoiningsubpixels in the respective ones of the disjoint regions as analyteinterior subpixels belonging to a same analyte, and storing thecategorization and downscaled location coordinates of the analyteinterior subpixels in the memory on an analyte-by-analyte basis for usein base calling the analytes. In one implementation, the methodincludes, on the analyte-by-analyte basis, determining distances of theanalyte interior subpixels from respective ones of the centers of theanalytes, and storing the distances in the memory on theanalyte-by-analyte basis for use in base calling the analytes.

In one implementation, the method includes extracting intensities fromthe analyte interior subpixels in the respective ones of the disjointregions, including using at least one of nearest neighbor intensityextraction, Gaussian based intensity extraction, intensity extractionbased on average of 2×2 subpixel area, intensity extraction based onbrightest of 2×2 subpixel area, intensity extraction based on average of3×3 subpixel area, bilinear intensity extraction, bicubic intensityextraction, and/or intensity extraction based on weighted area coverage,and storing the intensities in the memory on the analyte-by-analytebasis for use in base calling the analytes.

In one implementation, the method includes based on the disjointregions, determining, as part of the related analyte metadata, spatialdistribution of the analytes, including at least one of analyte shapes,analyte sizes, and/or analyte boundaries, and storing the relatedanalyte metadata in the memory on the analyte-by-analyte basis for usein base calling the analytes.

In one implementation, the input image data includes images in thesequence of image sets, and the images have a resolution of 3000×3000.In one implementation, the input image data includes at least one imagepatch from each of the images in the sequence of image sets, and theimage patch covers a portion of the tile and has a resolution of 20×20.In one implementation, the input image data includes an upsampledrepresentation of the image patch from each of the images in thesequence of image sets, and the upsampled representation has aresolution of 80×80. In one implementation, the output has an upsampledresolution of 80×80.

In one implementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, the encoder subnetwork includes ahierarchy of encoders, and the decoder subnetwork includes a hierarchyof decoders that map low resolution encoder feature maps to full inputresolution feature maps. In one implementation, the density of theanalytes ranges from about 100,000 analytes/mm² to about 1,000,000analytes/mm². In another implementation, the density of the analytesranges from about 1,000,000 analytes/mm² to about 10,000,000analytes/mm².

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Training Regression Model

We disclose a computer-implemented method of training a neural networkto identify analytes and related analyte metadata. The method includesobtaining training data for training the neural network. The trainingdata includes a plurality of training examples and corresponding groundtruth data that should be generated by the neural network by processingthe training examples. Each training example includes image data from asequence of image sets. Each image in the sequence of image sets coversa tile of a flow cell and depicts intensity emissions of analytes on thetile and their surrounding background captured for a particular imagechannel at a particular one of a plurality of sequencing cycles of asequencing run performed on the flow cell. Each ground truth dataidentifies analytes, whose intensity emissions are depicted by the imagedata of a corresponding training example, as disjoint regions ofadjoining subpixels, centers of the analytes as center subpixels atcenters of mass of the respective ones of the disjoint regions, andtheir surrounding background as background subpixels not belonging toany of the disjoint regions. The method includes using a gradientdescent training technique to train the neural network and generatingoutputs for the training examples that progressively match the groundtruth data, including iteratively optimizing a loss function thatminimizes error between the outputs and the ground truth data, andupdating parameters of the neural network based on the error.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the method includes, upon error convergence aftera final iteration, storing the updated parameters of the neural networkin memory to be applied to further neural network-based templategeneration and base calling. In one implementation, in the ground truthdata, the adjoining subpixels in the respective ones of the disjointregions have intensity values weighted according to distance of anadjoining subpixel from a center subpixel in a disjoint region to whichthe adjoining subpixel belongs. In one implementation, in the groundtruth data, the center subpixels have highest intensity values withinthe respective ones of the disjoint regions. In one implementation, inthe ground truth data, the background subpixels all have a same lowestintensity value in the output. In one implementation, in the groundtruth data, the intensity values are normalized between zero and one.

In one implementation, the loss function is mean squared error and theerror is minimized on a subpixel-basis between the normalized intensityvalues of corresponding subpixels in the outputs and the ground truthdata. In one implementation, the ground truth data identify, as part ofthe related analyte metadata, spatial distribution of the analytes,including at least one of analyte shapes, analyte sizes, and/or analyteboundaries. In one implementation, the image data includes images in thesequence of image sets, and the images have a resolution of 1800×1800.In one implementation, the image data includes at least one image patchfrom each of the images in the sequence of image sets, and the imagepatch covers a portion of the tile and has a resolution of 20×20. In oneimplementation, the image data includes an upsampled representation ofthe image patch from each of the images in the sequence of image sets,and the upsampled representation of the image patch has a resolution of80×80.

In one implementation, in the training data, multiple training examplesrespectively include as image data different image patches from eachimage in a sequence of image sets of a same tile, and at least some ofthe different image patches overlap with each other. In oneimplementation, the ground truth data has an upsampled resolution of80×80. In one implementation, the training data includes trainingexamples for a plurality of tiles of the flow cell. In oneimplementation, the training data includes training examples for avariety of flow cells, sequencing instruments, sequencing protocols,sequencing chemistries, sequencing reagents, and analyte densities. Inone implementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, the encoder subnetwork includes ahierarchy of encoders, and the decoder subnetwork includes a hierarchyof decoders that map low resolution encoder feature maps to full inputresolution feature maps for subpixel-wise classification by a finalclassification layer.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Neural Network-Based Template Generator

We disclose a computer-implemented method of determining metadata aboutanalytes on a flow cell. The method includes accessing image data thatdepicts intensity emissions of the analytes, processing the image datathrough one or more layers of a neural network and generating analternative representation of the image data, and processing thealternative representation through an output layer and generating anoutput that identifies at least one of shapes and sizes of the analytesand/or centers of the analytes.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the image data further depicts intensityemissions of surrounding background of the analytes. In such animplementation, the method includes the output identifying spatialdistribution of the analytes on the flow cell, including the surroundingbackground and boundaries between the analytes. In one implementation,the method includes determining center location coordinates of theanalytes on the flow cell based on the output. In one implementation,the neural network is a convolutional neural network. In oneimplementation, the neural network is a recurrent neural network. In oneimplementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, followed by the output layer, the encodersubnetwork includes a hierarchy of encoders, and the decoder subnetworkincludes a hierarchy of decoders that map low resolution encoder featuremaps to full input resolution feature maps.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Binary Classification Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the imagedata. In one implementation, each image in the sequence of image setscovers the tile, and depicts intensity emissions of analytes on the tileand their surrounding background captured for a particular image channelat a particular one of a plurality of sequencing cycles of a sequencingrun performed on the flow cell. The method includes processing thealternative representation through a classification layer and generatingan output that identifies centers of analytes whose intensity emissionsare depicted by the input image data. The output has a plurality ofsubpixels, and each subpixel in the plurality of subpixels is classifiedas either an analyte center or a non-center.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the classification layer assigns each subpixel inthe output a first likelihood score of being the analyte center, and asecond likelihood score of being the non-center. In one implementation,the first and second likelihood scores are determined based on a softmaxfunction and exponentially normalized between zero and one. In oneimplementation, the first and second likelihood scores are determinedbased on a sigmoid function and normalized between zero and one. In oneimplementation, each subpixel in the output is classified as either theanalyte center or the non-center based on which one of the first andsecond likelihood scores is higher than the other. In oneimplementation, each subpixel in the output is classified as either theanalyte center or the non-center based on whether the first and secondlikelihood scores are above a predetermined threshold likelihood score.In one implementation, the output identifies the centers at centers ofmass of respective ones of the analytes. In one implementation, in theoutput, subpixels classified as analyte centers are assigned a samefirst predetermined value, and subpixels classified as non-centers areall assigned a same second predetermined value. In one implementation,the first and second predetermined values are intensity values. In oneimplementation, the first and second predetermined values are continuousvalues.

In one implementation, the method includes determining locationcoordinates of subpixels classified as analyte centers, downscaling thelocation coordinates by an upsampling factor used to prepare the inputimage data, and storing the downscaled location coordinates in memoryfor use in base calling the analytes. In one implementation, the inputimage data includes images in the sequence of image sets, and the imageshave a resolution of 3000×3000. In one implementation, the input imagedata includes at least one image patch from each of the images in thesequence of image sets, and the image patch covers a portion of the tileand has a resolution of 20×20. In one implementation, the input imagedata includes an upsampled representation of the image patch from eachof the images in the sequence of image sets, and the upsampledrepresentation has a resolution of 80×80. In one implementation, theoutput has an upsampled resolution of 80×80.

In one implementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, followed by the classification layer, theencoder subnetwork includes a hierarchy of encoders, and the decodersubnetwork includes a hierarchy of decoders that map low resolutionencoder feature maps to full input resolution feature maps forsubpixel-wise classification by the classification layer. In oneimplementation, the density of the analytes ranges from about 100,000analytes/mm² to about 1,000,000 analytes/mm². In another implementation,the density of the analytes ranges from about 1,000,000 analytes/mm² toabout 10,000,000 analytes/mm².

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Training Binary Classification Model

We disclose a computer-implemented method of training a neural networkto identify analytes and related analyte metadata. The method includesobtaining training data for training the neural network. The trainingdata includes a plurality of training examples and corresponding groundtruth data that should be generated by the neural network by processingthe training examples. Each training example includes image data from asequence of image sets. Each image in the sequence of image sets coversa tile of a flow cell and depicts intensity emissions of analytes on thetile and their surrounding background captured for a particular imagechannel at a particular one of a plurality of sequencing cycles of asequencing run performed on the flow cell. Each ground truth dataidentifies centers of analytes, whose intensity emissions are depictedby the image data of a corresponding training example. The ground truthdata has a plurality of subpixels, and each subpixel in the plurality ofsubpixels is classified as either an analyte center or a non-center. Themethod includes using a gradient descent training technique to train theneural network and generating outputs for the training examples thatprogressively match the ground truth data, including iterativelyoptimizing a loss function that minimizes error between the outputs andthe ground truth data, and updating parameters of the neural networkbased on the error.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the method includes, upon error convergence aftera final iteration, storing the updated parameters of the neural networkin memory to be applied to further neural network-based templategeneration and base calling. In one implementation, in the ground truthdata, subpixels classified as analyte centers are all assigned a samefirst predetermined class score, and subpixels classified as non-centersare all assigned a same second predetermined class score. In oneimplementation, in each output, each subpixel has a first predictionscore of being the analyte center, and a second prediction score ofbeing the non-center. In one implementation, the loss function is customweighted binary cross entropy loss and the error is minimized on asubpixel-basis between the prediction scores and the class scores ofcorresponding subpixels in the outputs and the ground truth data. In oneimplementation, the ground truth data identifies the centers at centersof mass of respective ones of the analytes. In one implementation, inthe ground truth data, subpixels classified as analyte centers are allassigned a same first predetermined value, and subpixels classified asnon-centers are all assigned a same second predetermined value. In oneimplementation, the first and second predetermined values are intensityvalues. In another implementation, the first and second predeterminedvalues are continuous values.

In one implementation, the ground truth data identify, as part of therelated analyte metadata, spatial distribution of the analytes,including at least one of analyte shapes, analyte sizes, and/or analyteboundaries. In one implementation, the image data includes images in thesequence of image sets, and the images have a resolution of 1800×1800.In one implementation, the image data includes at least one image patchfrom each of the images in the sequence of image sets, and the imagepatch covers a portion of the tile and has a resolution of 20×20. In oneimplementation, the image data includes an upsampled representation ofthe image patch from each of the images in the sequence of image sets,and the upsampled representation of the image patch has a resolution of80×80. In one implementation, in the training data, multiple trainingexamples respectively include as image data different image patches fromeach image in a sequence of image sets of a same tile, and at least someof the different image patches overlap with each other. In oneimplementation, the ground truth data has an upsampled resolution of80×80. In one implementation, the training data includes trainingexamples for a plurality of tiles of the flow cell. In oneimplementation, the training data includes training examples for avariety of flow cells, sequencing instruments, sequencing protocols,sequencing chemistries, sequencing reagents, and analyte densities. Inone implementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, followed by a classification layer, theencoder subnetwork includes a hierarchy of encoders, and the decodersubnetwork includes a hierarchy of decoders that map low resolutionencoder feature maps to full input resolution feature maps forsubpixel-wise classification by the classification layer.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Ternary Classification Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the imagedata. Each image in the sequence of image sets covers the tile, anddepicts intensity emissions of analytes on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. The method includes processing thealternative representation through a classification layer and generatingan output that identifies spatial distribution of analytes and theirsurrounding background whose intensity emissions are depicted by theinput image data, including at least one of analyte centers, analyteshapes, analyte sizes, and/or analyte boundaries. The output has aplurality of subpixels, and each subpixel in the plurality of subpixelsis classified as either background, analyte center, or analyte interior.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the classification layer assigns each subpixel inthe output a first likelihood score of being the background, a secondlikelihood score of being the analyte center, and a third likelihoodscore of being the analyte interior. In one implementation, the first,second, and third likelihood scores are determined based on a softmaxfunction and exponentially normalized between zero and one. In oneimplementation, each subpixel in the output is classified as either thebackground, the analyte center, or the analyte interior based on whichone among the first, second, and third likelihood scores is highest. Inone implementation, each subpixel in the output is classified as eitherthe background, the analyte center, or the analyte interior based onwhether the first, second, and third likelihood scores are above apredetermined threshold likelihood score. In one implementation, theoutput identifies the analyte centers at centers of mass of respectiveones of the analytes. In one implementation, in the output, subpixelsclassified as background are all assigned a same first predeterminedvalue, subpixels classified as analyte centers are all assigned a samesecond predetermined value, and subpixels classified as analyte interiorare all assigned a same third predetermined value. In oneimplementation, the first, second, and third predetermined values areintensity values. In one implementation, the first, second, and thirdpredetermined values are continuous values.

In one implementation, the method includes determining locationcoordinates of subpixels classified as analyte centers on ananalyte-by-analyte basis, downscaling the location coordinates by anupsampling factor used to prepare the input image data, and storing thedownscaled location coordinates in memory on the analyte-by-analytebasis for use in base calling the analytes. In one implementation, themethod includes determining location coordinates of subpixels classifiedas analyte interior on the analyte-by-analyte basis, downscaling thelocation coordinates by an upsampling factor used to prepare the inputimage data, and storing the downscaled location coordinates in memory onthe analyte-by-analyte basis for use in base calling the analytes. Inone implementation, the method includes, on the analyte-by-analytebasis, determining distances of the subpixels classified as analyteinterior from respective ones of the subpixels classified as analytecenters, and storing the distances in the memory on theanalyte-by-analyte basis for use in base calling the analytes. In oneimplementation, the method includes, on the analyte-by-analyte basis,extracting intensities from the subpixels classified as analyteinterior, including using at least one of nearest neighbor intensityextraction, Gaussian based intensity extraction, intensity extractionbased on average of 2×2 subpixel area, intensity extraction based onbrightest of 2×2 subpixel area, intensity extraction based on average of3×3 subpixel area, bilinear intensity extraction, bicubic intensityextraction, and/or intensity extraction based on weighted area coverage,and storing the intensities in the memory on the analyte-by-analytebasis for use in base calling the analytes.

In one implementation, the input image data includes images in thesequence of image sets, and the images have a resolution of 3000×3000.In one implementation, the input image data includes at least one imagepatch from each of the images in the sequence of image sets, and theimage patch covers a portion of the tile and has a resolution of 20×20.In one implementation, the input image data includes an upsampledrepresentation of the image patch from each of the images in thesequence of image sets, and the upsampled representation has aresolution of 80×80. In one implementation, the output has an upsampledresolution of 80×80. In one implementation, the neural network is a deepfully convolutional segmentation neural network with an encodersubnetwork and a corresponding decoder network, followed by theclassification layer, the encoder subnetwork includes a hierarchy ofencoders, and the decoder subnetwork includes a hierarchy of decodersthat map low resolution encoder feature maps to full input resolutionfeature maps for subpixel-wise classification by the classificationlayer. In one implementation, the density of the analytes ranges fromabout 100,000 analytes/mm² to about 1,000,000 analytes/mm². In anotherimplementation, the density of the analytes ranges from about 1,000,000analytes/mm² to about 10,000,000 analytes/mm².

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Training Ternary Classification Model

We disclose a computer-implemented method of training a neural networkto identify analytes and related analyte metadata. The method includesobtaining training data for training the neural network. The trainingdata includes a plurality of training examples and corresponding groundtruth data that should be generated by the neural network by processingthe training examples. Each training example includes image data from asequence of image sets. Each image in the sequence of image sets coversa tile of a flow cell and depicts intensity emissions of analytes on thetile and their surrounding background captured for a particular imagechannel at a particular one of a plurality of sequencing cycles of asequencing run performed on the flow cell. Each ground truth dataidentifies spatial distribution of analytes and their surroundingbackground whose intensity emissions are depicted by the input imagedata, including analyte centers, analyte shapes, analyte sizes, andanalyte boundaries. The ground truth data has a plurality of subpixels,and each subpixel in the plurality of subpixels is classified as eitherbackground, analyte center, or analyte interior. The method includesusing a gradient descent training technique to train the neural networkand generating outputs for the training examples that progressivelymatch the ground truth data, including iteratively optimizing a lossfunction that minimizes error between the outputs and the ground truthdata, and updating parameters of the neural network based on the error.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the method includes, upon error convergence aftera final iteration, storing the updated parameters of the neural networkin memory to be applied to further neural network-based templategeneration and base calling. In one implementation, in the ground truthdata, subpixels classified as background are all assigned a same firstpredetermined class score, subpixels classified as analyte centers areall assigned a same second predetermined class score, and subpixelsclassified as analyte interior are all assigned a same thirdpredetermined class score.

In one implementation, in each output, each subpixel has a firstprediction score of being the background, a second prediction score ofbeing the analyte center, and a third prediction score of being theanalyte interior. In one implementation, the loss function is customweighted ternary cross entropy loss and the error is minimized on asubpixel-basis between the prediction scores and the class scores ofcorresponding subpixels in the outputs and the ground truth data. In oneimplementation, the ground truth data identifies the analyte centers atcenters of mass of respective ones of the analytes. In oneimplementation, in the ground truth data, subpixels classified asbackground are all assigned a same first predetermined value, subpixelsclassified as analyte centers are all assigned a same secondpredetermined value, and subpixels classified as analyte interior areall assigned a same third predetermined value. In one implementation,the first, second, and third predetermined values are intensity values.In one implementation, the first, second, and third predetermined valuesare continuous values. In one implementation, the image data includesimages in the sequence of image sets, and the images have a resolutionof 1800×1800. In one implementation, the image data includes images inthe sequence of image sets, and the images have a resolution of1800×1800.

In one implementation, the image data includes at least one image patchfrom each of the images in the sequence of image sets, and the imagepatch covers a portion of the tile and has a resolution of 20×20. In oneimplementation, the image data includes an upsampled representation ofthe image patch from each of the images in the sequence of image sets,and the upsampled representation of the image patch has a resolution of80×80. In one implementation, in the training data, multiple trainingexamples respectively include as image data different image patches fromeach image in a sequence of image sets of a same tile, and at least someof the different image patches overlap with each other. In oneimplementation, the ground truth data has an upsampled resolution of80×80. In one implementation, the training data includes trainingexamples for a plurality of tiles of the flow cell. In oneimplementation, the training data includes training examples for avariety of flow cells, sequencing instruments, sequencing protocols,sequencing chemistries, sequencing reagents, and analyte densities. Inone implementation, the neural network is a deep fully convolutionalsegmentation neural network with an encoder subnetwork and acorresponding decoder network, followed by a classification layer, theencoder subnetwork includes a hierarchy of encoders, and the decodersubnetwork includes a hierarchy of decoders that map low resolutionencoder feature maps to full input resolution feature maps forsubpixel-wise classification by the classification layer.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Segmentation

We disclose a computer-implemented method of determining analytemetadata. The method includes processing input image data derived from asequence of image sets through a neural network and generating analternative representation of the input image data. The input image datahas an array of units that depicts analytes and their surroundingbackground. The method includes processing the alternativerepresentation through an output layer and generating an output valuefor each unit in the array. The method includes thresholding outputvalues of the units and classifying a first subset of the units asbackground units depicting the surrounding background. The methodincludes locating peaks in the output values of the units andclassifying a second subset of the units as center units containingcenters of the analytes. The method includes applying a segmenter to theoutput values of the units and determining shapes of the analytes asnon-overlapping regions of contiguous units separated by the backgroundunits and centered at the center units. The segmenter begins with thecenter units and determines, for each center unit, a group ofsuccessively contiguous units that depict a same analyte whose center iscontained in the center unit.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the units are pixels. In another implementation,the units are subpixels. In yet another implementation, the units aresuperpixels. In one implementation, the output values are continuousvalues. In another implementation, the output values are softmax scores.In one implementation, the contiguous units in the respective ones ofthe non-overlapping regions have output values weighted according todistance of a contiguous unit from a center unit in a non-overlappingregion to which the contiguous unit belongs. In one implementation, thecenter units have highest output values within the respective ones ofthe non-overlapping regions.

In one implementation, the non-overlapping regions have irregularcontours and the units are subpixels. In such an implementation, themethod includes determining analyte intensity of a given analyte byidentifying subpixels that contribute to the analyte intensity of thegiven analyte based on a corresponding non-overlapping region ofcontiguous subpixels that identifies a shape of the given analyte,locating the identified subpixels in one or more optical,pixel-resolution images generated for one or more image channels at acurrent sequencing cycle, in each of the images, interpolatingintensities of the identified subpixels, combining the interpolatedintensities, and normalizing the combined interpolated intensities toproduce a per-image analyte intensity for the given analyte in each ofthe images, and combining the per-image analyte intensity for each ofthe images to determine the analyte intensity of the given analyte atthe current sequencing cycle. In one implementation, the normalizing isbased on a normalization factor, and the normalization factor is anumber of the identified subpixels. In one implementation, the methodincludes base calling the given analyte based on the analyte intensityat the current sequencing cycle.

In one implementation, the non-overlapping regions have irregularcontours and the units are subpixels. In such an implementation, themethod includes determining analyte intensity of a given analyte byidentifying subpixels that contribute to the analyte intensity of thegiven analyte based on a corresponding non-overlapping region ofcontiguous subpixels that identifies a shape of the given analyte,locating the identified subpixels in one or more subpixel resolutionimages upsampled from corresponding optical, pixel-resolution imagesgenerated for one or more image channels at a current sequencing cycle,in each of the upsampled images, combining intensities of the identifiedsubpixels and normalizing the combined intensities to produce aper-image analyte intensity for the given analyte in each of theupsampled images, and combining the per-image analyte intensity for eachof the upsampled images to determine the analyte intensity of the givenanalyte at the current sequencing cycle. In one implementation, thenormalizing is based on a normalization factor, and the normalizationfactor is a number of the identified subpixels. In one implementation,the method includes base calling the given analyte based on the analyteintensity at the current sequencing cycle.

In one implementation, each image in the sequence of image sets covers atile, and depicts intensity emissions of analytes on a tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on a flow cell. In one implementation, the input image dataincludes at least one image patch from each of the images in thesequence of image sets, and the image patch covers a portion of the tileand has a resolution of 20×20. In one implementation, the input imagedata includes an upsampled, subpixel resolution representation of theimage patch from each of the images in the sequence of image sets, andthe upsampled, subpixel representation has a resolution of 80×80.

In one implementation, the neural network is a convolutional neuralnetwork. In another implementation, the neural network is a recurrentneural network. In yet another implementation, the neural network is aresidual neural network with residual bocks and residual connections. Inyet further implementation, the neural network is a deep fullyconvolutional segmentation neural network with an encoder subnetwork anda corresponding decoder network, the encoder subnetwork includes ahierarchy of encoders, and the decoder subnetwork includes a hierarchyof decoders that map low resolution encoder feature maps to full inputresolution feature maps.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Peak Detection

We disclose a computer-implemented method of determining analytemetadata. The method includes processing input image data derived from asequence of image sets through a neural network and generating analternative representation of the input image data. The input image datahas an array of units that depicts analytes and their surroundingbackground. The method includes processing the alternativerepresentation through an output layer and generating an output valuefor each unit in the array. The method includes thresholding outputvalues of the units and classifying a first subset of the units asbackground units depicting the surrounding background. The methodincludes locating peaks in the output values of the units andclassifying a second subset of the units as center units containingcenters of the analytes.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the method includes applying a segmenter to theoutput values of the units and determining shapes of the analytes asnon-overlapping regions of contiguous units separated by the backgroundunits and centered at the center units. The segmenter begins with thecenter units and determines, for each center unit, a group ofsuccessively contiguous units that depict a same analyte whose center iscontained in the center unit.

Other implementations of the method described in this section caninclude a non-transitory computer readable storage medium storinginstructions executable by a processor to perform any of the methodsdescribed above. Yet another implementation of the method described inthis section can include a system including memory and one or moreprocessors operable to execute instructions, stored in the memory, toperform any of the methods described above.

Neural Network-Based Analyte Metadata Generator

In one implementation, a method includes processing image data through aneural network and generating an alternative representation of the imagedata. The image data depicts intensity emissions of analytes. The methodincludes processing the alternative representation through an outputlayer and generating an output that identifies metadata about theanalytes, including at least one of spatial distribution of theanalytes, shapes of the analytes, centers of the analytes, and/orboundaries between the analytes. Each of the features discussed in theparticular implementation section for other implementations applyequally to this implementation. As indicated above, all the otherfeatures are not repeated here and should be considered repeated byreference. The reader will understand how features identified in theseimplementations can readily be combined with sets of base featuresidentified in other implementations. Other implementations of the methoddescribed in this section can include a non-transitory computer readablestorage medium storing instructions executable by a processor to performany of the methods described above. Yet another implementation of themethod described in this section can include a system including memoryand one or more processors operable to execute instructions, stored inthe memory, to perform any of the methods described above.

Units-Based Regression Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the inputimage data. Each image in the sequence of image sets covers the tile,and depicts intensity emissions of analytes on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. The method includes processing thealternative representation through an output layer and generating anoutput that identifies analytes, whose intensity emissions are depictedby the input image data, as disjoint regions of adjoining units, centersof the analytes as center units at centers of mass of the respectiveones of the disjoint regions, and their surrounding background asbackground units not belonging to any of the disjoint regions.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the units are pixels. In another implementation,the units are subpixels. In yet another implementation, the units aresuperpixels. Other implementations of the method described in thissection can include a non-transitory computer readable storage mediumstoring instructions executable by a processor to perform any of themethods described above. Yet another implementation of the methoddescribed in this section can include a system including memory and oneor more processors operable to execute instructions, stored in thememory, to perform any of the methods described above.

Units-Based Binary Classification Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the imagedata. Each image in the sequence of image sets covers the tile, anddepicts intensity emissions of analytes on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. The method includes processing thealternative representation through a classification layer and generatingan output that identifies centers of analytes whose intensity emissionsare depicted by the input image data. The output has a plurality ofunits, and each unit in the plurality of units is classified as eitheran analyte center or a non-center.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the units are pixels. In another implementation,the units are subpixels. In yet another implementation, the units aresuperpixels. Other implementations of the method described in thissection can include a non-transitory computer readable storage mediumstoring instructions executable by a processor to perform any of themethods described above. Yet another implementation of the methoddescribed in this section can include a system including memory and oneor more processors operable to execute instructions, stored in thememory, to perform any of the methods described above.

Units-Based Ternary Classification Model

We disclose a computer-implemented method of identifying analytes on atile of a flow cell and related analyte metadata. The method includesprocessing input image data from a sequence of image sets through aneural network and generating an alternative representation of the imagedata. Each image in the sequence of image sets covers the tile, anddepicts intensity emissions of analytes on the tile and theirsurrounding background captured for a particular image channel at aparticular one of a plurality of sequencing cycles of a sequencing runperformed on the flow cell. The method includes processing thealternative representation through a classification layer and generatingan output that identifies spatial distribution of analytes and theirsurrounding background whose intensity emissions are depicted by theinput image data, including at least one of analyte centers, analyteshapes, analyte sizes, and/or analyte boundaries. The output has aplurality of units, and each unit in the plurality of units isclassified as either background, analyte center, or analyte interior.

Each of the features discussed in the particular implementation sectionfor other implementations apply equally to this implementation. Asindicated above, all the other features are not repeated here and shouldbe considered repeated by reference. The reader will understand howfeatures identified in these implementations can readily be combinedwith sets of base features identified in other implementations.

In one implementation, the units are pixels. In another implementation,the units are subpixels. In yet another implementation, the units aresuperpixels. Other implementations of the method described in thissection can include a non-transitory computer readable storage mediumstoring instructions executable by a processor to perform any of themethods described above. Yet another implementation of the methoddescribed in this section can include a system including memory and oneor more processors operable to execute instructions, stored in thememory, to perform any of the methods described above.

CLAUSES

We disclose the following clauses:

Clauses Set 1

1. A computer-implemented method of determining image regions indicativeof analytes on a tile of a flow cell, the method comprising:

accessing a series of image sets generated during a sequencing run, eachimage set in the series generated during a respective sequencing cycleof the sequencing run, each image in the series depicting the analytesand their surrounding background, and each image in the series having aplurality of subpixels;

obtaining, from a base caller, a base call classifying each of thesubpixels, thereby producing a base call sequence for each of thesubpixels across a plurality of sequencing cycles of the sequencing run;

determining a plurality of disjointed regions of contiguous subpixelswhich share a substantially matching base call sequence; and

generating an analyte map identifying the determined disjointed regions.

2. The computer-implemented method of clause 1, further including:

training a classifier based upon the determined plurality of disjointedregions of contiguous subpixels, the classifier being a neuralnetwork-based template generator for processing input image data togenerate a decay map, a ternary map, or a binary map, representing oneor more properties of each of a plurality of analytes represented in theinput image data for base calling by a neural network-based base caller,

preferably in order to increase the level of throughput inhigh-throughput nucleic acid sequencing technologies.

3. The computer-implemented method of any of clauses 1-2, furtherincluding:

generating the analyte map by identifying as background those subpixelsthat do not belong to any of the disjointed regions.

4. The computer-implemented method of any of clauses 1-3, wherein theanalyte map identifies analyte boundary portions between two contiguoussubpixels whose base call sequences do not substantially match.5. The computer-implemented method of any of clauses 1-4, wherein thedetermining the plurality of disjointed regions of contiguous subpixelsfurther includes:

identifying origin subpixels at preliminary center coordinates of theanalytes determined by the base caller; and

breadth-first searching for substantially matching base call sequencesby beginning with the origin subpixels and continuing with successivelycontiguous non-origin subpixels.

6. The computer-implemented method of any of clauses 1-5, furtherincluding:

determining hyperlocated center coordinates of the analytes bycalculating centers of mass of the disjointed regions of the analyte mapas an average of coordinates of respective contiguous subpixels formingthe disjointed regions; and

storing the hyperlocated center coordinates of the analytes in thememory for use as ground truth for training the classifier.

7. The computer-implemented method of clause 6, further including:

identifying centers of mass subpixels in the disjointed regions of theanalyte map at the hyperlocated center coordinates of the analytes;

upsampling the analyte map using interpolation and storing the upsampledanalyte map in the memory for use as ground truth for training theclassifier; and

in the upsampled analyte map, assigning a value to each contiguoussubpixel in the disjointed regions based on a decay factor that isproportional to distance of a contiguous subpixel from a center of masssubpixel in a disjointed region to which the contiguous subpixelbelongs.

8. The computer-implemented method of clause 7, the method morepreferably further including:

generating the decay map from the upsampled analyte map that expressesthe contiguous subpixels in the disjointed regions and the subpixelsidentified as the background based on their assigned values; and

storing the decay map in the memory for use as ground truth for trainingthe classifier.

9. The computer-implemented method of clause 8, the method even morepreferably further including:

in the upsampled analyte map, categorizing, on the analyte-by-analytebasis, the contiguous subpixels in the disjointed regions as analyteinterior subpixels belonging to a same analyte, the centers of masssubpixels as analyte center subpixels, subpixels containing the analyteboundary portions as boundary subpixels, and the subpixels identified asthe background as background subpixels; and

storing the categorizations in the memory for use as ground truth fortraining the classifier.

10. The computer-implemented method of any of clauses 1-9, furtherincluding:

storing, on the analyte-by-analyte basis, coordinates of the analyteinterior subpixels, the analyte center subpixels, the boundarysubpixels, and the background subpixels in the memory for use as groundtruth for training the classifier;

downscaling the coordinates by a factor used to upsample the analytemap; and

storing, on the analyte-by-analyte basis, the downscaled coordinates inthe memory for use as ground truth for training the classifier.

11. The computer-implemented method of any of clauses 1-10, furtherincluding:

in a binary ground truth data generated from the upsampled analyte map,using color coding to label the analyte center subpixels as belonging toan analyte center class and all other subpixels are belonging to anon-center class; and

storing the binary ground truth data in the memory for use as groundtruth for training the classifier.

12. The computer-implemented method of any of clauses 1-11, furtherincluding:

in a ternary ground truth data generated from the upsampled analyte map,using color coding to label the background subpixels as belonging to abackground class, the analyte center subpixels as belonging to ananalyte center class, and the analyte interior subpixels as belonging toan analyte interior class; and

storing the ternary ground truth data in the memory for use as groundtruth for training the classifier.

13. The computer-implemented method of any of clauses 1-12, furtherincluding:

generating analyte maps for a plurality of tiles of the flow cell;

storing the analyte maps in memory and determining spatial distributionof analytes in the tiles based on the analyte maps, including theirshapes and sizes;

in the upsampled analyte maps of the analytes in the tiles,categorizing, on an analyte-by-analyte basis, subpixels as analyteinterior subpixels belonging to a same analyte, analyte centersubpixels, boundary subpixels, and background subpixels;

storing the categorizations in the memory for use as ground truth fortraining the classifier;

storing, on the analyte-by-analyte basis across the tiles, coordinatesof the analyte interior subpixels, the analyte center subpixels, theboundary subpixels, and the background subpixels in the memory for useas ground truth for training the classifier;

downscaling the coordinates by the factor used to upsample the analytemap; and

storing, on the analyte-by-analyte basis across the tiles, thedownscaled coordinates in the memory for use as ground truth fortraining the classifier.

14. The computer-implemented method of any of clauses 1-13, wherein thebase call sequences are substantially matching when a predeterminedportion of base calls match on an ordinal position-wise basis.15. The computer-implemented method of any of clauses 1-14, wherein thedetermining the plurality of disjointed regions of contiguous subpixelswhich share a substantially matching base call sequence is based upon apredetermined minimum number of subpixels for a disjointed region.16. The computer-implemented method of any of clauses 1-15, wherein theflow cell has at least one patterned surface with an array of wells thatoccupy the analytes, further including:

based on the determined shapes and sizes of the analytes, determining

-   -   which ones of the wells are substantially occupied by at least        one analyte,    -   which ones of the wells are minimally occupied, and    -   which ones of the wells are co-occupied by multiple analytes.        17. A computer-implemented method of determining metadata about        analytes on a tile of a flow cell, the method comprising:

accessing a set of images of the tile captured during a sequencing runand preliminary center coordinates of the analytes determined by a basecaller;

for each image set, obtaining, from a base caller, a base callclassifying, as one of four bases,

-   -   origin subpixels that contain the preliminary center coordinates        and    -   a predetermined neighborhood of contiguous subpixels that are        successively contiguous to respective ones of the origin        subpixels,    -   thereby producing a base call sequence for each of the origin        subpixels and for each of the predetermined neighborhood of        contiguous subpixels;

generating an analyte map that identifies the analytes as disjointedregions of contiguous subpixels that

-   -   are successively contiguous to at least some of the respective        ones of the origin subpixels and    -   share a substantially matching base call sequence of the one of        four bases with the at least some of the respective ones of the        origin subpixels; and

storing the analyte map in memory and determining the shapes and thesizes of the analytes based on the disjointed regions in the analytemap.

18. A computer-implemented method of generating training data for neuralnetwork-based template generation and base calling, the methodcomprising:

accessing a multitude of images of a flow cell captured over a pluralityof cycles of a sequencing run, the flow cell having a plurality of tilesand, in the multitude of images, each of the tiles having a sequence ofimage sets generated over the plurality of cycles, and each image in thesequence of image sets depicting intensity emissions of analytes andtheir surrounding background on a particular one of the tiles at aparticular one the cycles;

constructing a training set having a plurality of training examples,each training example corresponding to a particular one of the tiles andincluding image data from at least some image sets in the sequence ofimage sets of the particular one of the tiles; and

generating at least one ground truth data representation for each of thetraining examples, the ground truth data representation identifying atleast one property of analytes on the particular one of the tiles whoseintensity emissions are depicted by the image data and being determinedat least in part using the method of any of clauses 1-17.

19. The computer-implemented method of clause 18, wherein the at leastone property of analytes is selected from the group consisting of:spatial distribution of analytes on the tile; analyte shape; analytesize; analyte boundary; and center of contiguous regions including asingle analyte.20. The computer-implemented method of any of clauses 18-19, wherein theimage data includes images in each of the at least some image sets inthe sequence of image sets of the particular one of the tiles.21. The computer-implemented method of any of clauses 18-20, wherein theimage data includes at least one image patch from each of the images.22. The computer-implemented method of any of clauses 18-21, wherein theimage data includes an upsampled representation of the image patch.23. The computer-implemented method of any of clauses 18-22, whereinmultiple training examples correspond to a same particular one of thetiles and respectively include as image data different image patchesfrom each image in each of at least some image sets in a sequence ofimage sets of the same particular one of the tiles, and

wherein at least some of the different image patches overlap with eachother.

24. The computer-implemented method of any of clauses 18-23, wherein theground truth data representation identifies the analytes as disjointregions of adjoining subpixels, the centers of the analytes as centersof mass subpixels within respective ones of the disjoint regions, andtheir surrounding background as subpixels that do not belong to any ofthe disjoint regions.25. The computer-implemented method of any of clauses 18-24, furtherincluding:

storing, in memory, the training examples in the training set andassociated ground truth data representations as the training data forthe neural network-based template generation and base calling.

26. A computer-implemented method, including:

accessing sequencing images of analytes produced by a sequencer;

generating training data from the sequencing images; and

using the training data for training a neural network to generatemetadata about the analytes.

27. A computer-implemented method, including:

accessing sequencing images of analytes produced by a sequencer;

generating training data from the sequencing images; and

using the training data for training a neural network to base call theanalytes.

28. A computer-implemented method of determining image regionsindicative of analytes on a tile of a flow cell, the method comprising:

accessing a series of image sets generated during a sequencing run, eachimage set in the series generated during a respective sequencing cycleof the sequencing run, each image in the series depicting the analytesand their surrounding background, and each image in the series having aplurality of subpixels;

obtaining, from a base caller, a base call classifying each of thesubpixels, thereby producing a base call sequence for each of thesubpixels across a plurality of sequencing cycles of the sequencing run;and

determining a plurality of disjointed regions of contiguous subpixelswhich share a substantially matching base call sequence.

Clauses Set 2

1. A computer-implemented method of generating ground truth trainingdata to train a neural network-based template generator for clustermetadata determination task, the method comprising:

accessing a series of image sets generated during a sequencing run, eachimage set in the series generated during a respective sequencing cycleof the sequencing run, each image in the series depicting clusters andtheir surrounding background, each image in the series having pixels ina pixel domain, and each of the pixels is divided into a plurality ofsubpixels in a subpixel domain;

obtaining, from a base caller, a base call classifying each of thesubpixels as one of four bases (A, C, T, and G), thereby producing abase call sequence for each of the subpixels across a plurality ofsequencing cycles of the sequencing run;

generating a cluster map that identifies the clusters as disjointedregions of contiguous subpixels which share a substantially matchingbase call sequence;

determining cluster metadata based on the disjointed regions in thecluster map,

-   -   wherein the cluster metadata includes cluster centers, cluster        shapes, cluster sizes, cluster background, and/or cluster        boundaries; and

using the cluster metadata to generate ground truth training data fortraining a neural network-based template generator for cluster metadatadetermination task,

-   -   wherein the ground truth training data comprises a decay map, a        ternary map, or a binary map,    -   wherein the neural network-based template generator is trained        to produce the decay map, the ternary map, or the binary map as        output based on the ground truth training data, and    -   wherein, upon execution of the cluster metadata determination        task during inference, the cluster metadata is in turn        determined from the decay map, the ternary map, or the binary        map that are produced as the output by the trained neural        network-based template generator.        2. The computer-implemented method of claim 1, further        including:

using the cluster metadata derived from the decay map, the ternary map,or the binary map produced as the output by the neural network-basedtemplate generator for base calling by a neural network-based basecaller, in order to increase throughput in high-throughput nucleic acidsequencing technologies.

3. The computer-implemented method of claim 1, further including:

generating the cluster map by identifying as background those subpixelsthat do not belong to any of the disjointed regions.

4. The computer-implemented method of claim 1, wherein the cluster mapidentifies cluster boundary portions between two contiguous subpixelswhose base call sequences do not substantially match.5. The computer-implemented method of claim 1, wherein the cluster mapis generated based on:

identifying origin subpixels at preliminary center coordinates of theclusters determined by the base caller; and

breadth-first searching for substantially matching base call sequencesby beginning with the origin subpixels and continuing with successivelycontiguous non-origin subpixels.

6. The computer-implemented method of claim 1, further including:

determining hyperlocated center coordinates of the clusters bycalculating centers of mass of the disjointed regions of the cluster mapas an average of coordinates of respective contiguous subpixels formingthe disjointed regions; and

storing the hyperlocated center coordinates of the clusters in thememory for use as the ground truth training data for training the neuralnetwork-based template generator.

7. The computer-implemented method of claim 6, further including:

identifying centers of mass subpixels in the disjointed regions of thecluster map at the hyperlocated center coordinates of the clusters;

upsampling the cluster map using interpolation and storing the upsampledcluster map in the memory for use as the ground truth training data fortraining the neural network-based template generator; and

in the upsampled cluster map, assigning a value to each contiguoussubpixel in the disjointed regions based on a decay factor that isproportional to distance of a contiguous subpixel from a center of masssubpixel in a disjointed region to which the contiguous subpixelbelongs.

8. The computer-implemented method of claim 7, further including:

generating the decay map from the upsampled cluster map that expressesthe contiguous subpixels in the disjointed regions and the subpixelsidentified as the background based on their assigned values; and

storing the decay map in the memory for use as the ground truth trainingdata for training the neural network-based template generator.

9. The computer-implemented method of claim 8, further including:

in the upsampled cluster map, categorizing, on the cluster-by-clusterbasis, the contiguous subpixels in the disjointed regions as clusterinterior subpixels belonging to a same cluster, the centers of masssubpixels as cluster center subpixels, subpixels containing the clusterboundary portions as boundary subpixels, and the subpixels identified asthe background as background subpixels; and

storing the categorizations in the memory for use as the ground truthtraining data for training the neural network-based template generator.

10. The computer-implemented method of claim 9, further including:

storing, on the cluster-by-cluster basis, coordinates of the clusterinterior subpixels, the cluster center subpixels, the boundarysubpixels, and the background subpixels in the memory for use as theground truth training data for training the neural network-basedtemplate generator;

downscaling the coordinates by a factor used to upsample the clustermap; and

storing, on the cluster-by-cluster basis, the downscaled coordinates inthe memory for use as the ground truth training data for training theneural network-based template generator.

11. The computer-implemented method of claim 10, further including:

generating cluster maps for a plurality of tiles of the flow cell;

storing the cluster maps in memory and determining the cluster metadataof clusters in the tiles based on the cluster maps, including thecluster centers, the cluster shapes, the cluster sizes, the clusterbackground, and/or the cluster boundaries;

in the upsampled cluster maps of the clusters in the tiles,categorizing, on a cluster-by-cluster basis, subpixels as clusterinterior subpixels belonging to a same cluster, cluster centersubpixels, boundary subpixels, and background subpixels;

storing the categorizations in the memory for use as the ground truthtraining data for training the neural network-based template generator;

storing, on the cluster-by-cluster basis across the tiles, coordinatesof the cluster interior subpixels, the cluster center subpixels, theboundary subpixels, and the background subpixels in the memory for useas the ground truth training data for training the neural network-basedtemplate generator;

downscaling the coordinates by the factor used to upsample the clustermap; and

storing, on the cluster-by-cluster basis across the tiles, thedownscaled coordinates in the memory for use as the ground truthtraining data for training the neural network-based template generator.

12. The computer-implemented method of claim 11, wherein the base callsequences are substantially matching when a predetermined portion ofbase calls match on an ordinal position-wise basis.13. The computer-implemented method of claim 1, wherein the cluster mapis generated based upon a predetermined minimum number of subpixels fora disjointed region.14. The computer-implemented method of claim 1, wherein the flow cellhas at least one patterned surface with an array of wells that occupythe clusters, further including:

based on the determined shapes and sizes of the clusters, determining

-   -   which ones of the wells are substantially occupied by at least        one cluster,    -   which ones of the wells are minimally occupied, and    -   which ones of the wells are co-occupied by multiple clusters.        15. A computer-implemented method of determining metadata about        clusters on a tile of a flow cell, the method comprising:

accessing a set of images of the tile captured during a sequencing runand preliminary center coordinates of the clusters determined by a basecaller;

for each image set, obtaining, from a base caller, a base callclassifying, as one of four bases,

-   -   origin subpixels that contain the preliminary center coordinates        and    -   a predetermined neighborhood of contiguous subpixels that are        successively contiguous to respective ones of the origin        subpixels,    -   thereby producing a base call sequence for each of the origin        subpixels and for each of the predetermined neighborhood of        contiguous subpixels;

generating a cluster map that identifies the clusters as disjointedregions of contiguous subpixels that

-   -   are successively contiguous to at least some of the respective        ones of the origin subpixels and    -   share a substantially matching base call sequence of the one of        four bases with the at least some of the respective ones of the        origin subpixels; and

storing the cluster map in memory and determining the shapes and thesizes of the clusters based on the disjointed regions in the clustermap.

16. A computer-implemented method of generating training data for neuralnetwork-based template generation and base calling, the methodcomprising:

accessing a multitude of images of a flow cell captured over a pluralityof cycles of a sequencing run, the flow cell having a plurality of tilesand, in the multitude of images, each of the tiles having a sequence ofimage sets generated over the plurality of cycles, and each image in thesequence of image sets depicting intensity emissions of clusters andtheir surrounding background on a particular one of the tiles at aparticular one the cycles;

constructing a training set having a plurality of training examples,each training example corresponding to a particular one of the tiles andincluding image data from at least some image sets in the sequence ofimage sets of the particular one of the tiles; and

generating at least one ground truth data representation for each of thetraining examples, the ground truth data representation identifying atleast one property of analytes on the particular one of the tiles whoseintensity emissions are depicted by the image data.

17. The computer-implemented method of claim 16, wherein the at leastone property of clusters is selected from the group consisting of:spatial distribution of clusters on the tile; cluster shape; clustersize; cluster boundary; and center of contiguous regions including asingle cluster.18. The computer-implemented method of claim 16, wherein the image dataincludes images in each of the at least some image sets in the sequenceof image sets of the particular one of the tiles.19. The computer-implemented method of claim 18, wherein the image dataincludes at least one image patch from each of the images.20. The computer-implemented method of claim 19, wherein the image dataincludes an upsampled representation of the image patch.21. The computer-implemented method of claim 16, wherein multipletraining examples correspond to a same particular one of the tiles andrespectively include as image data different image patches from eachimage in each of at least some image sets in a sequence of image sets ofthe same particular one of the tiles, and

wherein at least some of the different image patches overlap with eachother.

22. The computer-implemented method of claim 16, wherein the groundtruth data representation identifies the clusters as disjoint regions ofadjoining subpixels, the centers of the clusters as centers of masssubpixels within respective ones of the disjoint regions, and theirsurrounding background as subpixels that do not belong to any of thedisjoint regions.23. The computer-implemented method of claim 16, further including:

storing, in memory, the training examples in the training set andassociated ground truth data representations as the training data forthe neural network-based template generation and base calling.

24. A computer-implemented method, including:

accessing sequencing images of clusters produced by a sequencer;

generating training data from the sequencing images; and

using the training data for training a neural network to generatemetadata about the clusters.

25. A computer-implemented method, including:

accessing sequencing images of clusters produced by a sequencer;

generating training data from the sequencing images; and

using the training data for training a neural network to base call theclusters.

26. A computer-implemented method of determining image regionsindicative of analytes on a tile of a flow cell, the method comprising:

accessing a series of image sets generated during a sequencing run, eachimage set in the series generated during a respective sequencing cycleof the sequencing run, each image in the series depicting the analytesand their surrounding background, and each image in the series having aplurality of subpixels;

obtaining, from a base caller, a base call classifying each of thesubpixels, thereby producing a base call sequence for each of thesubpixels across a plurality of sequencing cycles of the sequencing run;

determining a plurality of disjointed regions of contiguous subpixelswhich share a substantially matching base call sequence; and

generating a cluster map identifying the determined disjointed regions.

Clauses Set 3

1. A neural network-implemented method of determining analyte data fromimage data generated based upon one or more analytes, the methodincluding:

receiving input image data, the input image data derived from a sequenceof images,

-   -   wherein each image in the sequence of images represents an        imaged region and depicts intensity emissions indicative of the        one or more analytes and a surrounding background of the        intensity emissions at a respective one of a plurality of        sequencing cycles of a sequencing run, and    -   wherein the input image data comprises image patches extracted        from each image in the sequence of images;

processing the input image data through a neural network to generate analternative representation of the input image data; and

processing the alternative representation through an output layer togenerate an output indicating properties of respective portions of theimaged region.

2. The neural network-implemented method of clause 1, wherein theproperties include

whether a portion represents background or analyte, and

whether a portion represents a center of a plurality of contiguous imageportions each representing a same analyte.

3. The neural network-implemented method of clause 1, wherein the outputidentifies

the one or more analytes, whose intensity emissions are depicted by theinput image data, as disjoint regions of adjoining units, centers of theone or more analytes as center units at centers of mass of therespective ones of the disjoint regions, and

the surrounding background of the intensity emissions as backgroundunits not belonging to any of the disjoint regions.

4. The neural network-implemented method of clause 3, wherein theadjoining units in the respective ones of the disjoint regions haveintensity values weighted according to distance of an adjoining unitfrom a center unit in a disjoint region to which the adjoining unitbelongs.5. The neural network-implemented method of any of clauses 1-4, whereinthe output is a binary map which classifies each portion as analyte orbackground.6. The neural network-implemented method of any of clauses 1-5, whereinthe output is a ternary map which classifies each portion as analyte,background, or center.7. The neural network-implemented method of any of clauses 1-6, furtherincluding:

applying a peak locator to the output to find peak intensities in theoutput;

determining location coordinates of the centers of the analytes based onthe peak intensities;

downscaling the location coordinates by an upsampling factor used toprepare the input image data; and

storing the downscaled location coordinates in memory for use in basecalling the analytes.

8. The neural network-implemented method of any of clauses 1-7, furtherincluding:

categorizing the adjoining units in the respective ones of the disjointregions as analyte interior units belonging to a same analyte; and

storing the categorization and downscaled location coordinates of theanalyte interior units in the memory on an analyte-by-analyte basis foruse in base calling the analytes.

9. The neural network-implemented method of any of clauses 1-8, furtherincluding:

obtaining training data for training the neural network,

-   -   wherein the training data includes a plurality of training        examples and corresponding ground truth data,    -   wherein each training example includes image data from a        sequence of image sets,        -   wherein each image in the sequence of image sets represents            a tile of a flow cell and depicts intensity emissions of            analytes on the tile and their surrounding background            captured for a particular image channel at a particular one            of a plurality of sequencing cycles of a sequencing run            performed on the flow cell, and        -   wherein each ground truth data identifies properties of            respective portions of the training examples; and    -   using a gradient descent training technique to train the neural        network and generating outputs for the training examples that        progressively match the ground truth data, including iteratively        -   optimizing a loss function that minimizes error between the            outputs and the ground truth data, and        -   updating parameters of the neural network based on the            error.            10. The neural network-implemented method of any of clauses            1-9, wherein the properties comprise identifying whether a            unit is a center or a non-center.            11. The neural network-implemented method of clause 9,            further including:

upon error convergence after a final iteration, storing the updatedparameters of the neural network in memory to be applied to furtherneural network-based template generation and base calling.

12. The neural network-implemented method of any of clauses 9-11,wherein, in the ground truth data, the adjoining units in the respectiveones of the disjoint regions have intensity values weighted according todistance of an adjoining unit from a center unit in a disjoint region towhich the adjoining unit belongs.13. The neural network-implemented method of any of clauses 9-11,wherein, in the ground truth data, the center units have highestintensity values within the respective ones of the disjoint regions.14. The neural network-implemented method of any of clauses 9-13,wherein the loss function is mean squared error and the error isminimized on a unit-basis between the normalized intensity values ofcorresponding units in the outputs and the ground truth data.15. The neural network-implemented method of any of clauses 9-14,wherein, in the training data, multiple training examples respectivelyinclude as image data different image patches from each image in asequence of image sets of a same tile, and

-   -   wherein at least some of the different image patches overlap        with each other.        16. The neural network-implemented method of any of clauses        9-15, wherein, in the ground truth data,

units classified as analyte centers are all assigned a same firstpredetermined class score, and

units classified as non-centers are all assigned a same secondpredetermined class score.

17. The neural network-implemented method of any of clauses 9-16,wherein the loss function is custom-weighted binary cross-entropy lossand the error is minimized on a unit-basis between the prediction scoresand the class scores of corresponding units in the outputs and theground truth data.18. The neural network-implemented method of any of clauses 9-17,wherein, in the ground truth data,

units classified as background are all assigned a same firstpredetermined class score,

units classified as analyte centers are all assigned a same secondpredetermined class score, and

units classified as analyte interior are all assigned a same thirdpredetermined class score.

19. The neural network-implemented method of any of clauses 1-18,further including:

thresholding output values of the units and classifying a first subsetof the units as background units depicting the surrounding background;

locating peaks in the output values of the units and classifying asecond subset of the units as center units containing centers of theanalytes; and

applying a segmenter to the output values of the units and determiningshapes of the analytes as non-overlapping regions of contiguous unitsseparated by the background units and centered at the center units,wherein the segmenter begins with the center units and determines, foreach center unit, a group of successively contiguous units that depict asame analyte whose center is contained in the center unit.

20. The neural network-implemented method of any of clauses 1-19,wherein the non-overlapping regions have irregular contours and theunits are units, further including:

determining analyte intensity of a given analyte by:

-   -   identifying units that contribute to the analyte intensity of        the given analyte based on a corresponding non-overlapping        region of contiguous units that identifies a shape of the given        analyte;    -   locating the identified units in one or more optical, pixel        resolution images generated for one or more image channels at a        current sequencing cycle;    -   in each of the images, interpolating intensities of the        identified units, combining the interpolated intensities, and        normalizing the combined interpolated intensities to produce a        per-image analyte intensity for the given analyte in each of the        images; and    -   combining the per-image analyte intensity for each of the images        to determine the analyte intensity of the given analyte at the        current sequencing cycle.        21. The neural network-implemented method of any of clauses        1-20, wherein the non-overlapping regions have irregular        contours and the units are units, further including:

determining analyte intensity of a given analyte by:

-   -   identifying units that contribute to the analyte intensity of        the given analyte based on a corresponding non-overlapping        region of contiguous units that identifies a shape of the given        analyte;    -   locating the identified units in one or more unit resolution        images upsampled from corresponding optical, pixel resolution        images generated for one or more image channels at a current        sequencing cycle;    -   in each of the upsampled images, combining intensities of the        identified units and normalizing the combined intensities to        produce a per-image analyte intensity for the given analyte in        each of the upsampled images; and    -   combining the per-image analyte intensity for each of the        upsampled images to determine the analyte intensity of the given        analyte at the current sequencing cycle.        22. The neural network-implemented method of any of clauses        1-21, wherein the normalizing is based on a normalization        factor, and

wherein the normalization factor is a number of the identified units.

23. The neural network-implemented method of any of clauses 1-22,further including:

base calling the given analyte based on the analyte intensity at thecurrent sequencing cycle.

24. A neural network-implemented method of determining metadata aboutanalytes on a flow cell, the method including:

accessing image data that depicts intensity emissions of the analytes;

processing the image data through one or more layers of a neural networkand generating an alternative representation of the image data; and

processing the alternative representation through an output layer andgenerating an output that identifies at least one of shapes and sizes ofthe analytes and/or centers of the analytes.

25. The neural network-implemented method of clause 24, wherein theimage data further depicts intensity emissions of surrounding backgroundof the analytes, further including:

the output identifying spatial distribution of the analytes on the flowcell, including the surrounding background and boundaries between theanalytes.

26. A computer-implemented method, including:

processing image data through a neural network and generating analternative representation of the image data, wherein the image datadepicts intensity emissions of analytes; and

processing the alternative representation through an output layer andgenerating an output that identifies metadata about the analytes,including at least one of spatial distribution of the analytes, shapesof the analytes, centers of the analytes, and/or boundaries between theanalytes.

27. A neural network-implemented method of determining cluster metadatafrom image data generated based upon one or more clusters, the methodincluding:

receiving input image data, the input image data derived from a sequenceof images,

-   -   wherein each image in the sequence of images represents an        imaged region and depicts intensity emissions of the one or more        clusters and their surrounding background at a respective one of        a plurality of sequencing cycles of a sequencing run, and    -   wherein the input image data comprises image patches extracted        from each image in the sequence of images;

processing the input image data through a neural network to generate analternative representation of the input image data, wherein the neuralnetwork is trained for cluster metadata determination task, includingdetermining cluster background, cluster centers, and cluster shapes;

processing the alternative representation through an output layer togenerate an output indicating properties of respective portions of theimaged region;

thresholding output values of the output and classifying a first subsetof the respective portions of the imaged region as background portionsdepicting the surrounding background;

locating peaks in the output values of the output and classifying asecond subset of the respective portions of the imaged region as centerportions containing centers of the clusters; and

applying a segmenter to the output values of the output and determiningshapes of the clusters as non-overlapping regions of contiguous portionsof the imaged region separated by the background portions and centeredat the center portions.

What is claimed is:
 1. A neural network-implemented method ofdetermining cluster metadata from image data generated based upon one ormore clusters, the method including: receiving input image data, theinput image data derived from a sequence of images, wherein each imagein the sequence of images represents an imaged region and depictsintensity emissions of the one or more clusters and their surroundingbackground at a respective one of a plurality of sequencing cycles of asequencing run, and wherein the input image data comprises image patchesextracted from each image in the sequence of images; processing theinput image data through a neural network to generate an alternativerepresentation of the input image data, wherein the neural network istrained for cluster metadata determination task, including determiningcluster background, cluster centers, and cluster shapes; processing thealternative representation through an output layer to generate an outputindicating properties of respective portions of the imaged region;thresholding output values of the output and classifying a first subsetof the respective portions of the imaged region as background portionsdepicting the surrounding background; locating peaks in the outputvalues of the output and classifying a second subset of the respectiveportions of the imaged region as center portions containing centers ofthe clusters; and applying a segmenter to the output values of theoutput and determining shapes of the clusters as non-overlapping regionsof contiguous portions of the imaged region separated by the backgroundportions and centered at the center portions.
 2. The neuralnetwork-implemented method of claim 1, wherein the properties includewhether a portion represents background or cluster, and whether aportion represents a center of a plurality of contiguous image portionseach representing a same cluster.
 3. The neural network-implementedmethod of claim 1, wherein the output identifies the one or moreclusters, whose intensity emissions are depicted by the input imagedata, as disjoint regions of adjoining units, centers of the one or moreclusters as center units at centers of mass of the respective ones ofthe disjoint regions, and their surrounding background as backgroundunits not belonging to any of the disjoint regions.
 4. The neuralnetwork-implemented method of claim 3, wherein the adjoining units inthe respective ones of the disjoint regions have intensity valuesweighted according to distance of an adjoining unit from a center unitin a disjoint region to which the adjoining unit belongs.
 5. The neuralnetwork-implemented method of claim 1, wherein the output is a binarymap which classifies each portion as cluster or background.
 6. Theneural network-implemented method of claim 1, wherein the output is aternary map which classifies each portion as cluster, background, orcenter.
 7. The neural network-implemented method of claim 1, furtherincluding: applying a peak locator to the output to find peakintensities in the output; determining location coordinates of thecenters of the clusters based on the peak intensities; downscaling thelocation coordinates by an upsampling factor used to prepare the inputimage data; and storing the downscaled location coordinates in memoryfor use in base calling the clusters.
 8. The neural network-implementedmethod of claim 7, further including: categorizing the adjoining unitsin the respective ones of the disjoint regions as cluster interior unitsbelonging to a same cluster; and storing the categorization anddownscaled location coordinates of the cluster interior units in thememory on an cluster-by-cluster basis for use in base calling theclusters.
 9. The neural network-implemented method of claim 1, furtherincluding: obtaining training data for training the neural network,wherein the training data includes a plurality of training examples andcorresponding ground truth data, wherein each training example includesimage data from a sequence of image sets, wherein each image in thesequence of image sets represents a tile of a flow cell and depictsintensity emissions of clusters on the tile and their surroundingbackground captured for a particular image channel at a particular oneof a plurality of sequencing cycles of a sequencing run performed on theflow cell, and wherein each ground truth data identifies properties ofrespective portions of the training examples; and using a gradientdescent training technique to train the neural network and generatingoutputs for the training examples that progressively match the groundtruth data, including iteratively optimizing a loss function thatminimizes error between the outputs and the ground truth data, andupdating parameters of the neural network based on the error.
 10. Theneural network-implemented method of claim 9, wherein the propertiescomprise clusters, whose intensity emissions are depicted by the imagedata of a corresponding training example, as disjoint regions ofadjoining units, centers of the clusters as center units at centers ofmass of the respective ones of the disjoint regions, and theirsurrounding background as background units not belonging to any of thedisjoint regions.
 11. The neural network-implemented method of claim 10,wherein the properties comprise identifying whether a unit is a centeror a non-center.
 12. The neural network-implemented method of claim 11,further including: upon error convergence after a final iteration,storing the updated parameters of the neural network in memory to beapplied to further neural network-based template generation and basecalling.
 13. The neural network-implemented method of claim 12, wherein,in the ground truth data, the adjoining units in the respective ones ofthe disjoint regions have intensity values weighted according todistance of an adjoining unit from a center unit in a disjoint region towhich the adjoining unit belongs.
 14. The neural network-implementedmethod of claim 13, wherein, in the ground truth data, the center unitshave highest intensity values within the respective ones of the disjointregions.
 15. The neural network-implemented method of claim 14, whereinthe loss function is mean squared error and the error is minimized on aunit-basis between the normalized intensity values of correspondingunits in the outputs and the ground truth data.
 16. The neuralnetwork-implemented method of claim 15, wherein, in the training data,multiple training examples respectively include as image data differentimage patches from each image in a sequence of image sets of a sametile, and wherein at least some of the different image patches overlapwith each other.
 17. The neural network-implemented method of claim 16,wherein, in the ground truth data, units classified as cluster centersare all assigned a same first predetermined class score, and unitsclassified as non-centers are all assigned a same second predeterminedclass score.
 18. The neural network-implemented method of claim 17,wherein the loss function is custom weighted binary cross entropy lossand the error is minimized on a unit-basis between the prediction scoresand the class scores of corresponding units in the outputs and theground truth data.
 19. The neural network-implemented method of claim18, wherein, in the ground truth data, units classified as backgroundare all assigned a same first predetermined class score, unitsclassified as cluster centers are all assigned a same secondpredetermined class score, and units classified as cluster interior areall assigned a same third predetermined class score.
 20. The neuralnetwork-implemented method of claim 19, further including: thresholdingoutput values of the units and classifying a first subset of the unitsas background units depicting the surrounding background; locating peaksin the output values of the units and classifying a second subset of theunits as center units containing centers of the clusters; and applying asegmenter to the output values of the units and determining shapes ofthe clusters as non-overlapping regions of contiguous units separated bythe background units and centered at the center units, wherein thesegmenter begins with the center units and determines, for each centerunit, a group of successively contiguous units that depict a samecluster whose center is contained in the center unit.
 21. The neuralnetwork-implemented method of claim 20, wherein the non-overlappingregions have irregular contours and the units are units, furtherincluding: determining cluster intensity of a given cluster by:identifying units that contribute to the cluster intensity of the givencluster based on a corresponding non-overlapping region of contiguousunits that identifies a shape of the given cluster; locating theidentified units in one or more optical, pixel resolution imagesgenerated for one or more image channels at a current sequencing cycle;in each of the images, interpolating intensities of the identifiedunits, combining the interpolated intensities, and normalizing thecombined interpolated intensities to produce a per-image clusterintensity for the given cluster in each of the images; and combining theper-image cluster intensity for each of the images to determine thecluster intensity of the given cluster at the current sequencing cycle.22. The neural network-implemented method of claim 21, wherein thenon-overlapping regions have irregular contours and the units are units,further including: determining cluster intensity of a given cluster by:identifying units that contribute to the cluster intensity of the givencluster based on a corresponding non-overlapping region of contiguousunits that identifies a shape of the given cluster; locating theidentified units in one or more unit resolution images upsampled fromcorresponding optical, pixel resolution images generated for one or moreimage channels at a current sequencing cycle; in each of the upsampledimages, combining intensities of the identified units and normalizingthe combined intensities to produce a per-image cluster intensity forthe given cluster in each of the upsampled images; and combining theper-image cluster intensity for each of the upsampled images todetermine the cluster intensity of the given cluster at the currentsequencing cycle.
 23. The neural network-implemented method of claim 22,wherein the normalizing is based on a normalization factor, and whereinthe normalization factor is a number of the identified units.
 24. Theneural network-implemented method of claim 23, further including: basecalling the given cluster based on the cluster intensity at the currentsequencing cycle.